WO2025207164A2 - Systèmes et procédés pour caractérisations associées à un matériau quantique - Google Patents

Systèmes et procédés pour caractérisations associées à un matériau quantique

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Publication number
WO2025207164A2
WO2025207164A2 PCT/US2024/058440 US2024058440W WO2025207164A2 WO 2025207164 A2 WO2025207164 A2 WO 2025207164A2 US 2024058440 W US2024058440 W US 2024058440W WO 2025207164 A2 WO2025207164 A2 WO 2025207164A2
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WIPO (PCT)
Prior art keywords
quantum
data
sensor
properties
target material
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Pending
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PCT/US2024/058440
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English (en)
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WO2025207164A3 (fr
WO2025207164A9 (fr
Inventor
Max Dorn Adam BARNETT
Kyle Michael Scheps
Raphael Scheps
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Octagon I/o Ltd
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Octagon I/o Ltd
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Publication date
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Publication of WO2025207164A2 publication Critical patent/WO2025207164A2/fr
Publication of WO2025207164A3 publication Critical patent/WO2025207164A3/fr
Publication of WO2025207164A9 publication Critical patent/WO2025207164A9/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B28—WORKING CEMENT, CLAY, OR STONE
    • B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
    • B28C7/00—Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
    • B28C7/0007—Pretreatment of the ingredients, e.g. by heating, sorting, grading, drying, disintegrating; Preventing generation of dust
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B28—WORKING CEMENT, CLAY, OR STONE
    • B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
    • B28C7/00—Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
    • B28C7/02—Controlling the operation of the mixing
    • B28C7/022—Controlling the operation of the mixing by measuring the consistency or composition of the mixture, e.g. with supply of a missing component
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B28—WORKING CEMENT, CLAY, OR STONE
    • B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
    • B28C7/00—Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
    • B28C7/02—Controlling the operation of the mixing
    • B28C7/022—Controlling the operation of the mixing by measuring the consistency or composition of the mixture, e.g. with supply of a missing component
    • B28C7/024—Controlling the operation of the mixing by measuring the consistency or composition of the mixture, e.g. with supply of a missing component by measuring properties of the mixture, e.g. moisture, electrical resistivity, density
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B28—WORKING CEMENT, CLAY, OR STONE
    • B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
    • B28C9/00—General arrangement or layout of plant
    • B28C9/002—Mixing systems, i.e. flow charts or diagrams; Making slurries; Involving methodical aspects; Involving pretreatment of ingredients; Involving packaging

Definitions

  • the one or more quantum states may be prepared prior to the interaction with the target material. Additionally, or alternatively, the interaction of the one or more quantum states with the target material may be direct or indirect. [0006] In some embodiments, the one or more quantum states may indirectly interact or directly interact with at least one material coupled to the target material. Further, the one or more first data elements may be indicative of a field strength, an atomic composition, a molecular composition, and/or a disposition of constituents of the target material. Additionally, or alternatively, the one or more first data elements may be indicative of a field strength, an atomic composition, a molecular composition, and/or a disposition of constituents of the target material at a plurality of locations in space.
  • the method may include accessing a database storing material identification data associated with a plurality of material identifiers and determining material composition data associated with the material identifier. Additionally, or alternatively, generating the first output may include providing the material identifier data, the material properties in the first set of contextual conditions, and the material composition data to the first model, where the first model may be an artificial intelligence (AI) model, simulating, via the first model, the material composition data in a second set of contextual conditions, generating one or more material properties, via the simulation, of the target material in the second set of contextual conditions, and generating the first output including one or more first output data entries associated with the one or more material properties for the target material in the second set of contextual conditions.
  • AI artificial intelligence
  • the one or more first data elements may include material property data of the target material in one or more sets of contextual conditions. Further, generating the first output may include providing the material property data in one or more sets of contextual conditions to the first model, where the first model may be an artificial intelligence (AI) model, simulating, via the first model, the material property data, generating one or more material identifiers, via the simulation, corresponding to the material property data, and generating the first output including one or more first output data entries associated with the one or more material identifiers.
  • the one or more generated material identifiers may include one or more material identifiers stored in a database.
  • the one or more generated material identifiers may include one or more candidate material identifiers, where the one or more candidate material identifiers may be absent from a database, and where the method may include generating a plurality of material composition data for each candidate material identifier of the one or more candidate material identifier and generating the first output including one or more additional first output data entries associated with the plurality of material composition data for each candidate material identifier.
  • the method may include generating a compositional arrangement for the plurality of material composition data for each candidate material identifier of the one or more candidate material identifiers, where the compositional arrangement may include a sequence of chemical equations for each of the candidate material identifiers.
  • compositional arrangement for each candidate material identifier may include one or more method sequences for manufacturing the candidate material identifier. Additionally, or alternatively, the method may include applying an input, via one or more actuators, to one or more locations on the target material, sensing, via one or more quantum sensors, a field associated with the actuated target material, inputting data associated with the sensed field into an artificial intelligence (AI) model, where the AI model may be configured to reconstruct the field of a portion of the target material using the data, outputting a reconstructed field of the portion of the target material from the AI model to a material prediction model, and outputting from the material prediction model a characterization of the portion of the target material.
  • AI artificial intelligence
  • the one or more actuators may be electromagnetic antennas
  • the input may be an electromagnetic wave
  • the one or more quantum sensors may be a quantum sensor array configured to sense an evolution of the field in a plurality of locations due to input electromagnetic wave.
  • the method may include preparing the one or more quantum states via a quantum photonic device including an exotic light generation module operably coupled to a microcontroller unit (MCU), where the MCU may be configured to determine one or more new quantum states to generate and control the exotic light generation module to generate the one or more new quantum states.
  • MCU microcontroller unit
  • Figure 1 illustrates an example system for quantum material-related characterizations in accordance with an example embodiment of the present disclosure
  • Figure 2 illustrates a block diagram of example circuitry (e.g., server circuitry) that may be specifically configured in accordance with one or more example embodiments of the present disclosure
  • Figure 3 illustrates example configurations for quantum sensor arrays in accordance with some embodiments of the present disclosure
  • Figure 4 illustrates example configurations for multi-modal quantum sensor arrays in accordance with some embodiments of the present disclosure
  • Figure 5 illustrates an example quantum sensing element for a nitrogen-vacancy (NV) center diamond setup in accordance with some embodiments of the present disclosure
  • Figure 6 illustrates an example method for a sensing array in proximity with a target material in accordance with some embodiments of the present disclosure
  • Figure 7 illustrates an example method for reconstructing a three-dimensional target material using a sensor array in accordance with some embodiments of the present disclosure
  • Figure 8 illustrate
  • the present disclosure is directed to quantum material-related characterizations using devices (e.g., classical devices, quantum devices, and/or hybrid devices), and the use of device generated data (e.g., data from classical devices, quantum devices, and/or hybrid devices) as inputs to software and/or AI-implemented systems and methods for material property prediction, material selection, material optimization, and/or material generation.
  • the software and/or AI-implemented methods described herein may include the use of a sensor device.
  • the methods may include the use of a sensor device to gather data associated with a material, in some cases, the material being the material under consideration, or a material similar and/or associated with the material under consideration.
  • the devices may, for example, be photonic sensors, mechanical sensors, wave- based sensors, quantum sensors, hybrid devices, and/or the like.
  • photonic sensors and/or hyperspectral sensors may be used to gather information associated with the compositional material properties of materials.
  • the sensors described herein may be installed at different stages of a material's lifecycle (e.g., a finished product and the performance of the finished product, raw materials, and/or during material production).
  • the present disclosure may be applicable to any material where a material may be understood in the common meaning of the word and/or may define an instance of a substance.
  • a material may, generally, be in a given state or phase, for example in a solid, liquid, gaseous, plasma, Bose-Einstein condensate state, and/or any other state understood as such by one of ordinary skill in the art in view of the present disclosure.
  • a material may undergo a state and/or phase transition in which the material may be actively transitioning between any two states.
  • the methods of the present disclosure may apply to any such material in any such state and/or state transition.
  • the properties of a material as it undergoes a phase transition from liquid to solid may be predicted.
  • Another embodiment may include generating a material design for a material in its solid, liquid, and/or gaseous forms for a given use case and/or given target properties.
  • the model may predict the melting and/or boiling points of the material as one of the material properties, accounting for variables including temperature, pressure, and/or the like. Further, the model may use different methods to simulate and/or predict properties of materials in different states, as well as use different internal representations to define the material's compositional properties. For example, a material may take the form of a crystal in solid form but may be amorphous in liquid form. Additional embodiments may include predicting the properties of a given plasma, selecting a material from a predetermined list of materials for a given use case, and/or inputting an existing material and generating an optimized version of the material according to some target use case, property, or other objective function.
  • Embodiments of the present disclosure may further describe matter in a subset of these states; however, it is to be understood that the methods herein are not restricted to only those materials that are further described.
  • examples of materials and/or material types for which the methods of the present disclosure may be applicable may include, but are not limited to, mixtures and/or compounds, mixes and/or composites, solutions, pure elements, crystals, metals, alloys, polymers (e.g., plastics, rubbers, elastomers, and/or the like), organic compounds, fertilizers, biomaterials, cementitious materials, edible materials (e.g., foods), ceramics papers, woods, minerals and/or stones, fuels, semiconductors, electronic materials, superconductors, textiles, glasses, transducer materials (e.g., any material that converts energy from one form to another), piezoelectric materials, magnetostrictive materials, electrochromic materials, materials used in electrical energy generation (e.g., materials involved in batteries and supercapacitors), photo
  • solids may broadly be categorized into crystals, quasi- crystals/semi-crystals/polycrystals, and/or amorphous solids. Further, crystals may be described as highly ordered materials, including a unit cell that is repeated periodically over extended regions with the same orientation. In some embodiments, amorphous solids may not include a regular repeating structure, though particles, atoms, molecules, and/or the like may be principally static in nature. Additionally, and/or alternatively, quasi-crystals and/or semi- crystals may denote materials in between crystals and amorphous solids that may include crystalline regions as well as non-crystalline or amorphous regions and/or regions with varying degrees of crystallinity.
  • Polycrystals may be described as materials including many individual crystallites of varying sizes and orientation, where crystallites (also referred to as grains) may have periodic, ordered structure.
  • the models described herein may use the degree of crystallinity as a measure of the crystalline structure of materials that may determine the internal representations through which a given material may be defined in the models herein. For example, perfect crystals may be defined internally through a geometrical representation of their unit cell, whilst amorphous structures may be defined using statistical methods and distributions, and materials in between may be defined using a combination of both.
  • crystals may be defined according to the atoms and spatial distribution in 3D representations of their unit cells.
  • defects may be artificially or naturally occurring in crystal structures.
  • the defects may be defined according to the spatial configuration and structural changes that may be induced by these defects, as well as through the chemical equations or nomenclature for said defects, and a measure associated with the occurrence rate of these defects (e.g., one in every million unit cells or other measures indicatives of how often these defects occur in numbers, volume, weight, density, and/or the like).
  • Defects may be introduced into crystalline structures in a process denoted doping that may be used to enhance one of or a plurality of crystal's properties. For example, doping may be done with relation to semiconductors.
  • the methods herein may include generating a crystalline structure design that may include doping defects to enhance a property.
  • the generated design may include the defect.
  • the functions of the generative models described herein may have equivalent and/or associated features for predictive models and/or any other model herein.
  • a predictive model may be used to predict properties of pure crystal, followed by which the properties resulting from the introduction of a doping defect may also be predicted. Additionally, or alternatively, the predictive model may predict the properties of doped crystals to begin with.
  • the method may generate the design for a pure crystal followed by a design adjustment that may include doping.
  • the materials may be described internally in the described systems through radial distribution functions (e.g., describing how atoms may be packed radially in a system).
  • Methods for generating radial distribution functions may include the use of Ornstein-Zernike Equations, Percus-Yevick approximations, and/or Hypernetted Chain Theory based methods.
  • radial distribution functions may be generated through empirical AI based methods based on data indicative of E&M scattering of materials to detect particle distributions and/or other methods that may be empirically measured using the methods described in wave-based sensing, quantum-based sensing, and/or using other sensor devices.
  • the models described herein may be used to create mappings or links between microscopic compositional information (e.g., microscopic compositional properties) to macroscopic properties (e.g., bulk properties).
  • these methods may use Kirkwood-Buff solution theory. Such methods may be used for chemical simulations and predictions in the models herein.
  • Said methods may also include the definition of structure factors and partition functions (e.g., defined through the product or sum over a particle distribution or the product or sum over integrals of distributions).
  • materials may be simulated by the models at any temperature, at any pressure, at any number of particles (e.g., atoms, molecules, ions, particles) N, at any volume, and/or may include any other contextual conditions (e.g., forces and/or fields being applied onto the material such as gravitational, electromagnetic, and/or weak and strong forces as well as forces in the mechanical sense of the term (e.g., forces from other objects)).
  • generalized radial distribution functions may be applicable to mixtures including a plurality of components, types of particles, structures, and/or the like, and may be used in the methods herein.
  • the models and methods herein may include using system symmetries to simplify calculations associated with the radial distribution function.
  • the radial distribution function is just one possible distribution functions, and other distribution functions (e.g., an angular distribution function) may be used to describe materials herein.
  • statistical approaches may be used on macroscopic scales, microscopic scales, and/or quantum scales to describe particle distributions.
  • operators may be represented through tensors and/or matrices, and evolutions may be represented via tensor products and/or tensor- vector products.
  • the architecture of the models described herein may be designed for efficient tensor product computation that may, in some embodiments, be done with the use of graphical tensor product methods and representations.
  • quantum simulation methods and quantum path integral simulations may be used for material simulations. Quantum-classical path integral methods may be used for material simulations, for example, when considering larger scales or volumes.
  • statistical methods may be used to determine a distribution of particle states or molecule states, and motion of said states.
  • motion determination may include the use of Brownian motion simulation methods, random walks, quantum walks, quantum-classical walks, and/or other associated methods and techniques described herein.
  • Prediction models as described herein may generally be used to predict and/or simulate the properties of materials based on compositional data associated with given materials.
  • Input data may be a material identifier (e.g., name, categorization of material, and/or the like), and/or any other composition data mentioned herein (e.g., 3D model of cell unit, partition function for particle distributions, and/or the like).
  • a material identifier may be provided, and compositional data may be retrieved (e.g., from a database), and, in other cases, the models may integrate with other systems to actively seek compositional data. Additionally, and/or alternatively, the model may be integrated with GPT-type models which may be able to provide the compositional data. [0065] In some embodiments, the models may also be used to simulate material behaviors in a given contextual condition. Such a process may be done for quantum simulations, as well as for macroscopic simulations for bulk properties. Additionally, and/or alternatively, stability may also be predicted including through predictions of the convex hull.
  • the predictive models may be able to take input properties in one set of contextual conditions and output a prediction for properties in any other set of contextual conditions. Additionally, and/or alternatively, the models may take as an input a chemical industrial process and predict the output of that process (e.g., state of the output material, properties of the output material, and/or the like). In some embodiments, training of the models may include experimental and/or theoretical data for material identifiers, compositions, and/or properties.
  • the models may ascertain with a high level of confidence that the measured composition matches the measured properties. If a compositional arrangement or composition is provided in a digital record, and associated properties are measured using sensors, it may be possible that the record may not accurately reflect the reality of the materials. In such embodiments, the models may use interpolation and/or generative methods to fill in the gaps. Additionally, and/or alternatively, models may use clustering methods to match compositions and properties with a higher degree of likelihood or confidence. [0069] In some embodiments, physico-chemical simulations may be used for training.
  • Said simulations may include finite element analysis-based simulations, quantum simulations, density functional theory-based simulations, quantum-classical hybrid simulations, and/or other simulation methods. By ingesting simulated data, the model may make up for missing volumes of data.
  • databases including material identifiers, material compositional data, and/or material property data may be labelled and fed to the models herein alongside sensor data and/or quantum sensor data matched for material identifiers.
  • the database may be incomplete and generative methods may be used to fill in the gaps using likeliest predicted outcomes.
  • the outputs for the models herein may include a list of predicted property attributes in a given contextual condition (e.g., standard temperature or properties).
  • a spatio-temporal simulation showing the behavior of the material in a given simulated contextual condition may be output for any volume or weight of material.
  • a predicted list of properties and/or attributes may be variable for a range of contextual conditions (e.g., may be able to see how every property changes with changing temperature, including phase transitions, and/or 3D models of molecules and bonds stretching and elongating as temperature increases).
  • the models herein may employ statistical methods, machine learning, density functional theory, quantum simulations, physico-chemical models (in some cases infused into statistical or machine learning methods), and/or any other kind of methods herein to produce the output.
  • Material property prediction may be described as a set of methods that may output a plurality of data indicative of expected properties of a material based on first data indicative of a material.
  • the first data indicative of a material may include data associated with the compositional material properties of a material and/or may include data associated with live features of a material (e.g., as measured by sensor devices).
  • the concepts and methods described herein may be described as generalizations of those described in mix optimization related techniques but may be used for any other material.
  • the generative models herein may be used to generate or discover a novel material design based on a given input.
  • the generated material design may include types of information such as compositional data associated with a given material, property data associated with a given material, and/or manufacturing instructions for a given material.
  • the model may take a prompt as input that may define target material objectives and/or use cases and/or desired properties. Further, if the initial prompt does not constrain the space of possible materials sufficiently, the model may converse with the user, asking questions until the model has sufficient information. Additionally, and/or alternatively, the input may be a set or combination of desired properties defined as targets. The desired properties may be target parameters within the models. The model may then be tasked to generate a material that most closely matches these parameters and/or properties.
  • the data may be used for training one or a plurality of AI models, where the models may predict properties and/or behaviors of materials in varying contextual conditions, select the optimal material from a predetermined set of materials based on a set of objective criteria, desired properties, and/or parameters or attributes, adjust and/or optimize the composition of a given material to design an alternative optimal material based on a set of objective criteria, desired properties, parameters and/or attributes, and/or generate a new material composition designed with one or more of some desired properties, parameters, and/or attributes, some desired behaviors in a given contextual condition, and/or an objective function for which the generated material is optimized.
  • the methods herein include generating a mapping between material composition and properties and/or attributes.
  • the methods herein include creating a compositional arrangement for a given material composition where the compositional arrangement may include a set of instructions for manufacturing the material.
  • the compositional arrangement may include steps to manufacture a material from raw constituents, where these steps may include the use of combustion ovens, mixers, electrolysis, and/or other processes involved in material creation.
  • the instructions may include a sequence of chemical reaction equations.
  • Quantum Methods, Systems, and Devices are further directed to the use of quantum sensors and metrology for characterization of materials and/or matter, including building materials (e.g., concrete, cement, steel, and/or the like), and the use of data generated from such sensors or metrology techniques as inputs and/or training data for any of the methods and/or systems described herein (e.g., any machine learning model for material generation, optimization, design, adjustment, and/or property prediction).
  • the quantum sensors may be utilized for material characterization, mix fingerprinting, sensor context awareness, contextual condition determination, and/or the like.
  • the use of quantum sensors may augment the accuracy and/or range of classical communication methods (e.g., through higher accuracy RF sensing).
  • quantum and/or classical interconnect or channels may communicate data from quantum and/or classical devices.
  • Embodiments of the present disclosure may use quantum algorithms and/or quantum models for material and/or mix simulation, generation, design, optimization, adjustment, property prediction, and/or other related methods described herein. Further, the quantum algorithms and/or quantum models may be executed on quantum computers of various kinds described below. Additionally, or alternatively, classical material algorithms may be adapted to quantum systems.
  • the present disclosure is directed to the use of quantum memory systems to store large volumes of data relating to materials (e.g., full chemical composition and/or atomic or molecular maps of materials), including the output of any of the quantum sensors / devices disclosed herein. Further, the present disclosure is directed to various material representations (e.g., multi-scale models, Lagrangian representations, and/or the like) and the use of such material representations in correlating properties of materials to their composition. [0088] Additionally, or alternatively, the present disclosure is directed to the use of quantum error correction algorithms in any of the systems described above.
  • present disclosure is directed to the use of quantum sensing methods for material characterization, such as quantum gravitometry, magnetometry, electrometry and/or thermometry.
  • quantum sensing methods for material characterization, such as quantum gravitometry, magnetometry, electrometry and/or thermometry.
  • the following definitions may all apply throughout this disclosure. These definitions are non-exhaustive and are cumulative to other definitions that may be clear to those of ordinary skill in the art.
  • the terms “mix,” “mixture,” “composite,” and similar terms may be used interchangeably to refer to a collection of materials (e.g., constituent components, constituent elements, constituent parts, etc.) that are combined together.
  • material designs may refer to proportions of constituent component parts associated with one or more targets for contextual material properties.
  • material designs may also include the steps (and associated timings) for mixing of a proportion of constituent components or raw materials.
  • any particular instantiation of a material formulation may include naturally variability in the proportions of constituent components for the same material formulation.
  • a “batch” may refer to a physical instantiation of a mix formulation.
  • a batch may include an associated volume and may often exist as a batch at the material manufacturer’s factory and throughout transit.
  • a “pour” may refer to a defined volume (e.g., at least partially enclosed via a mold, formwork, or otherwise) into which at least a portion of one or more batches of a mix formulation are provided.
  • a “pour” as described herein may be cured with the intent of forming an element of a structure (e.g., a building element).
  • a mix formulation, and the batches, pours, building elements, etc. associated with the mix formulation may further include various “material properties.”
  • the term “material property” may refer to any physical or chemical attribute, characteristic, parameter, feature, etc. of the materials described herein.
  • the material properties of a material may include one or more of static material properties, compositional material properties, contextual conditions, and/or contextual material properties as defined hereinafter. Although described herein with reference to an example framework for distinguishing between types or categories of material properties, for example static material properties vs. contextual material properties, the present disclosure contemplates that the devices, systems, methods, techniques, etc. of the present disclosure may be applicable to any determinable, measurable, and/or derivable attribute associated with building materials, formed of cementitious mixtures or otherwise.
  • static material property and “static property” may be used interchangeably to refer to any attribute, parameters, characteristic, state, and/or the like of a material (e.g., an example building material) that is independent of the context within which the material is used (e.g., an attribute that is context independent).
  • static material properties may include density (e.g., of water or other materials), particle size, homogeneity, fineness, specific gravity, natural variability, embodied carbon data, aggregate grading, porosity, and/or the like.
  • static material properties may include any context independent attribute of any type for any material.
  • compositional material property and “compositional property” may be used interchangeably to refer to any attribute, parameter, characteristic, state, and/or the like indicative of the proportions by which a material (e.g., a composite material as described herein) is composed of other materials (e.g., raw materials as defined herein).
  • a compositional material property may, for example, provide an indication of the mix formulation or compositions as defined herein at various levels of granularity.
  • the proportional relationship of constituent components or composition may be provided as a percentage of volume, by particle number, by mass, and/or any other relevant metric, relationship, etc.
  • the compositional material property may, for example, be provided as an absolute mass, mass density, or other representation.
  • information associated with the compositional material properties of a particular material may be provided by any relationship, proportionality, metrics, etc.
  • a cementitious mixture e.g., an example building material
  • compositional material properties of a material may further vary in time such that the above formulations described herein may further evolve in time.
  • a particular instantiation of a formulation e.g., a batch or the like
  • the material identifiers described herein that may, for example, be indicative of the formulation of a material may refer to a set of time- dependent compositional material properties for the material.
  • compositional material properties for a material that are determined by the techniques described herein may represent the formulation of a particular instantiation at the time at which the data on which the compositional material property is generated. Additionally, or alternatively, the compositional material properties may be representative of a theoretical or idealized formulation as associated with various target contextual material properties as defined herein (e.g., C80 concrete, C60 concrete, C40 concrete, etc.).
  • target contextual material properties e.g., C80 concrete, C60 concrete, C40 concrete, etc.
  • the terms “contextual material condition,” “contextual condition,” and “context” may be used interchangeably herein to refer to any imposed state or attribute that at least partially defines the instantiated context in which a material is used.
  • the contextual condition may, for example, be associated with various characteristics, attributes, aspects, etc.
  • Environmental data as an example contextual material condition may include meteorological data, such as ambient temperature data, humidity data, precipitation data, and/or other atmospheric effects (e.g., wind data, storm data, lightning data, etc.).
  • Environmental data may further include electromagnetic radiation data, data indicative of mechanical vibration and/or other mechanical disturbances, geological data (e.g., the type of soil surrounding foundations may impact its behavior), and/or oven data (e.g., instance in which ovens are used for curing, particularly in precast implementations).
  • Structural burden data as an example contextual material condition may include load data and/or load path data, stress data, strain data, and/or batching plant data (e.g., volume of batch, mixing data, mixing intensity data, rate of rotation, etc.).
  • contextual material properties may refer data indicative of compressive strength (e.g., 7-day strength, 28-day strength, 42-day strength, full strength profile, etc.), shrinkage, workability, tensile strength, flexural strength, stress, strain, calibration data related thereof, structural health, reactivity, flow rate, specific surface area, and/or the like.
  • compressive strength e.g., 7-day strength, 28-day strength, 42-day strength, full strength profile, etc.
  • shrinkage e.g., 7-day strength, 28-day strength, 42-day strength, full strength profile, etc.
  • workability e.g., 7-day strength, 28-day strength, 42-day strength, full strength profile, etc.
  • tensile strength e.g., tensile strength
  • flexural strength e.g., tensile strength
  • stress e.g., tensile strength
  • flexural strength e.g., tensile strength
  • stress e.g.,
  • target contextual material properties may therefore refer to a set of contextual material properties that are to be achieved (e.g., within applicable tolerances or the like) by the system, users, models, etc. described herein attempts to achieve for the particular mixture (e.g., as defined by mix identifier, mix classification, mix formulation, etc.).
  • raw material may be used to refer to any material described herein that is associated with only static material properties as defined above.
  • water, fly ash, sand, and/or the like may be raw materials in the databases and models described herein that are associated with only static material properties (e.g., density and pH, for example).
  • raw material and composite material may be interpreted by their physical or chemical meanings, namely, where a raw material is a component material used to make a product (wherein the product may be a composite material), and a composite material is a combination of two or more materials with different physical or chemical properties.
  • batch variability may be used to refer to the variability in the contextual material properties, the static material properties, and/or compositional material properties of a material (e.g., as defined by an associated formulation) across batches.
  • batch variability may result from the tolerances or other uncertainty of the quantities (e.g., the mixing proportion tolerances), the contextual conditions during batching, and/or also the natural variability in the properties of the raw material.
  • the embodiments of the present disclosure operate to account for batch variability in the performance of the operations described herein.
  • the terms “material identifier,” “material classification,” and/or the like may be used to refer to any mechanism of identifying a material, mixture, a family/type of material or mixtures, or any characterizing feature of materials or mixtures.
  • the material identifiers described herein provide information (e.g., data entries) regarding the particular materials (e.g., mix formulation) on which the models of the present disclosure are operating.
  • the models described herein may determine that a material formulation identified as C40 within the applicable database(s) will reach a minimum of 40 MPa within the contextual conditions described above (e.g., standard conditions).
  • this data associated with the material identifier for the material formulation may narrow a material’s expected strength performance over time in any given context (e.g., target contextual material properties), where such performance may be determined by the models described herein.
  • material space may refer to an N-dimensional space, such that all points in the domain of the N-dimensional space represent all possible material formulations (where such space may be an infinite space).
  • material space may refer to the space representing all possible cementitious mixtures used for construction, and whose N-dimensional coordinates include every material or non-material property that uniquely defines a material formulation (e.g., composition) in the models and databases described herein.
  • the material space may further include subcategories (e.g., material families, material types, and/or material classes) of materials in material-space (e.g., as defined by material properties, formulations, identifiers, or the like) that share at least one common characteristic.
  • subcategories e.g., material families, material types, and/or material classes
  • materials in material-space e.g., as defined by material properties, formulations, identifiers, or the like
  • first dataset and associated “first data entries” are used to refer to data that, in some embodiments, is received by the systems, models, etc. of the present disclosure as an input.
  • the first dataset may include data associated with various materials properties that are input by a user, generated by a sensor device (for example, a maturity or temperature sensor), other device, received from a database, received from a prior iteration of one or more of the models described herein, and/or the like, such as in the optimization and material identifier operations described herein.
  • the first dataset may include data generated by, received from or associated with a wave-based sensor (e.g. a mechanical or electromagnetic wave-based sensor configured to excite and/or measure a cementitious mixture, or configured to measure electrochemical or electromechanical parameters of a material).
  • a wave-based sensor e.g. a mechanical or electromagnetic wave-based sensor configured to excite and/or measure a cementitious mixture, or configured to measure electrochemical or electromechanical parameters of a material.
  • the first dataset may include data associated with sensor context awareness as described herein (e.g., data associated with a material, a pour implicating the material, an environment of the material, etc.). Additionally, or alternatively, in some embodiments, the first dataset and associated first data entries may be associated with a material identifier. Additionally, or alternatively, in some embodiments, the first dataset and associated first data entries may be associated with a spatial representation (e.g., a Building Information Modeling (BIM), floorplan or the like) as described herein.
  • BIM Building Information Modeling
  • the first dataset and associated first data entries may be associated with a measurement type of a material (e.g., a material identifying operation, a sensor device measurement or the like). Additionally, or alternatively, in some embodiments, the first dataset and associated first data entries may be associated with a structural progress flow as described herein. [00111] As would be evident to one of ordinary skill in the art in light of the present disclosure, the first dataset and associated first data entries may be associated with, indicative of, or otherwise related to any of the attributes, characteristics, parameters, metrics, etc. of the material operations, systems, devices, etc. described herein without limitation.
  • first dataset and associate first data entries may refer to the data structure by which data associated with the embodiments described herein is stored, regardless of data type, model used, system deployed, etc.
  • additional datasets e.g., second dataset or the like
  • the present disclosure contemplates that any number of different datasets of any type may be used by the embodiments herein.
  • any of the sensor devices described herein may be “smart sensors” that, for example, include a MCU, a memory, battery management, battery power, and one or more sensors types configured to perform the techniques described herein.
  • Each of the embodiments described herein may further be multivariate in which a plurality of sensors of the same or different type may be used.
  • the sensor devices of the present disclosure may be designed to be integrated throughout the lifecycle of a material, such as into concrete pours or elements (installed prior to pouring, (e.g., on rebar), and covered with concrete).
  • Various attachment methods are designed to ensure optimal functionality (e.g. wireless communication) and secure placement throughout the material lifecycle.
  • attachment designs consider factors such as sensor shape, resonance influence, and aggregate interference, in attachment selection and may make use of various materials and coatings.
  • These attachments are resilient to diverse material environmental conditions and do not compromise the host material’s structural integrity.
  • These attachments ensure secure attachment to different components of the material (e.g., elements of the pour structure such as reinforcement bars, otherwise referred to as ‘rebar’ or formwork, concrete drums in trucks etc.).
  • Attachment methods may include one or more of (1) straps, bands or ties; (2) clamps, clips and fasteners; (3) adhesive and welding techniques; (3) magnetic attachments (that snap on to rebar); (4) innovative materials; (5) other; and (6) floating or sinking configurations (no attachment).
  • Sensor devices may be used in association with “actuators” which as used herein may be used to refer to any element or circuitry component that is able to cause, generate, adjust and/or generally control any force, field or energy excitation or disturbance (including for example mechanical excitations, or electromagnetic excitations, and in particular wave- based excitations, through force or field couplings).
  • actuators as used herein may be used to refer to any element or circuitry component that is able to cause, generate, adjust and/or generally control any force, field or energy excitation or disturbance (including for example mechanical excitations, or electromagnetic excitations, and in particular wave- based excitations, through force or field couplings).
  • any element configured to or is otherwise capable of creating any form of excitation may be considered an “actuator.”
  • sensor, sensor device, transducer, actuator, and device may be used interchangeably to reference any of their respective meanings, in a context dependent way.
  • an example “transducer” may be intrinsically resonating in that the configuration of the transducer (e.g., by geometry or the like) produces or is otherwise associated with resonant behaviors (e.g., oscillatory resonance, wave-based resonance modes, etc.).
  • wave-based sensor may be used to refer to any device which may generate, adjust, or control a time-varying excitation (based on an input signal) and/or sense a response to an excitation including, but not limited to, of a target material, or another material coupled (directly or indirectly) to the target material.
  • a wave based sensor may be, used to generate or otherwise make use of and sense waves, excitations, and/or oscillations (such as electromagnetic waves, electric currents and/or mechanical stresses) as described herein.
  • wave-based may refer to any device, technique, sensory, etc. that employs one or more actuators to excite a host material, or a second material that is coupled to the host material.
  • the excitation may be a time varying signal (e.g., an oscillatory signal, a wave, etc.).
  • Wave-based devices, techniques, and sensing may also employ sensors to measure the response of the host material (directly, or indirectly through the response of the second material, or another material coupled to the host material).
  • the “wave-based” techniques described herein may encompass, without limitation, excitations, oscillations, and waves, and may further encompass any device configured to take input signals and generate, adjust, control an excitation of a field, force, or form of energy, such as via an actuator defined herein, as well as a response (e.g., material response, coupled medium response, etc.) to such excitation, oscillation, or wave.
  • a “data value” may include any piece of information relating to a measurable entity, such as an example temperature reading.
  • a “data type” may refer to a categorization of data values, such as thermal data for the example temperature reading.
  • the terms “data source” and “data entity” may be used interchangeably to refer to a data store that holds data values (e.g., a specific BIM model or the like).
  • the terms “data source type” and “data entity type” may be used interchangeably to refer to a categorization or type of data source or data entity.
  • a data entity may refer to as an instantiation of a data source (e.g., BIM model may be a class of data entities).
  • a “data element” may include a data value of a certain type stored within a data entity of a certain type (e.g., an element in a BIM model).
  • a data element may, for example, be continuous or discrete.
  • a discrete data element may include a data element that represents discrete information that is self-contained (e.g., a concrete cube test crush result).
  • a continuous data element may include a data element that represents continuous information that may be arbitrarily subdivided or combined (e.g., a slab in a BIM model may be subdivided into pours of arbitrary size).
  • a computing device is described herein as receiving data from another computing device, it will be appreciated that the data may be received directly from another computing device or may be received indirectly via one or more intermediary computing devices, such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and/or the like, sometimes referred to herein as a “network.”
  • intermediary computing devices such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and/or the like, sometimes referred to herein as a “network.”
  • the data may be sent directly to another computing device or may be sent indirectly via one or more intermediary computing devices, such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and/or the like.
  • Quantum Sensors may refer, depending on the context, to one or more of i) quantum sensor methods, techniques, systems, and/or devices, ii) quantum metrology methods, techniques, systems, and/or devices, iii) quantum communication methods, techniques, systems, and/or devices, iv) hybrid classico- quantum versions of any of the previous, and v) distributed networks of one or more of the forgoing (optionally coupled with classical systems including distributed systems).
  • quantum time transfer systems may include, but are not limited to atomic clocks, magnetometers, gravimeters, gyroscopes, quantum imaging sensors, quantum interferometers, quantum hall effect sensors, NV center diamond sensors, quantum thermometers, quantum radar systems, quantum positioning systems, quantum gravimeters, quantum accelerometers, quantum photodetectors, quantum electrometers, quantum voltage standards, quantum pressure sensors, quantum light detectors, quantum acoustic sensors, quantum spin sensors, quantum force sensors, quantum time standards, quantum gyrometers, quantum strain sensors, quantum spectroscopes, quantum hyperspectral imagers, quantum timing devices, quantum clocks, quantum frequency standards, and/or quantum time transfer systems.
  • atomic clocks magnetometers, gravimeters, gyroscopes, quantum imaging sensors, quantum interferometers, quantum hall effect sensors, NV center diamond sensors, quantum thermometers, quantum radar systems, quantum positioning systems, quantum gravimeters, quantum accelerometers, quantum photodetectors, quantum electrometers, quantum voltage standards,
  • Quantum Computer or “Quantum Computing” may refer, depending on the context, to computers that employ qubits (rather than bits) to execute algorithms and/or compute outputs.
  • This may include various implementations of quantum computers, including photonic quantum systems (e.g., photons make up the qubits and are transferred through quantum circuits), superconducting quantum computers (e.g., superconducting circuits make up the qubits and/or gates), trapped ion quantum computers (e.g., ions trapped in electromagnetic fields make up the qubits, and are operated on or manipulated using lasers to perform quantum operations), quantum annealers (e.g., where quantum annealing is used to find solutions to optimization problems that leverage quantum tunnelling), topological quantum computers (e.g., where qubits are made up of anyons with topological properties which are manipulated to carry out quantum computations), neutral atom quantum computers (e.g., where qubits are made up of neutral atoms trapped in optical lattices or
  • Quantum computers may also employ other technologies to create, store, transmit, receive, transfer, manipulate, operate on, and/or measure qubits, including: (i) those that may leverage any field (e.g., including any of those that result from the electromagnetic force, weak force, strong force, or gravitational force, as well as other fields of the standard model Lagrangian, or others beyond the standard model theories); (ii) encoding into particles, atoms, molecules, and/or other forms of matter and/or their quantum properties (e.g., spin, energy levels, vibrational modes, and/or the like).
  • any field e.g., including any of those that result from the electromagnetic force, weak force, strong force, or gravitational force, as well as other fields of the standard model Lagrangian, or others beyond the standard model theories
  • quantum properties e.g., spin, energy levels, vibrational modes, and/or the like.
  • quantum computer types contemplated by the present disclosure, superconducting quantum computers, trapped ion quantum computers, photonic quantum computers, quantum annealers, topological quantum computers, neutral atom quantum computers, spin qubit quantum computers, quantum dot quantum computers, diamond nitrogen-vacancy (NV) center quantum computers, molecular quantum computers, continuous variable quantum computers, hybrid quantum computers, and/or quantum reservoir computing.
  • quantum computer types contemplated by the present disclosure, superconducting quantum computers, trapped ion quantum computers, photonic quantum computers, quantum annealers, topological quantum computers, neutral atom quantum computers, spin qubit quantum computers, quantum dot quantum computers, diamond nitrogen-vacancy (NV) center quantum computers, molecular quantum computers, continuous variable quantum computers, hybrid quantum computers, and/or quantum reservoir computing.
  • NV diamond nitrogen-vacancy
  • quantum computers and quantum sensors, devices, and/or techniques may employ any number of quantum phenomena, including one or more of wave- particle duality, quantization, superposition, entanglement, the uncertainty principle, quantum tunnelling, discrete energy levels, spin, the Pauli exclusion principle, coherence and decoherence, non-locality, and/or the like.
  • Quantum Algorithms represent algorithms that are designed to exploit properties of quantum mechanics to speed up run-time and/or spatial efficiency. Further, said algorithms are designed to run faster and/or more efficiently on a quantum computer by exploiting the properties of qubits. They may exploit any number of quantum mechanical features, including superposition, entanglement, quantum interference, and quantum parallelism.
  • Examples may include Shor’s algorithm, Grover’s algorithm, quantum Fourier transform (QFT), quantum phase estimation, quantum simulation algorithms, and/or the like.
  • Quantum algorithms are, in principle, designed to operate on quantum computers and/or quantum systems, but may, in some embodiments, be executed on classical computers (e.g., through classical simulations of quantum computers). Further, quantum algorithms may also be executed across a hybrid computer and/or a distributed network of hybrid nodes that may be composed of CPUs, GPUs, and QPUs (quantum processing units). Hybrid algorithms may also be employed by embodiments of the present disclosure, with quantum subcomponents and/or other classical subcomponents.
  • compositional data and/or structural data may be gathered using sensors configured for X-ray diffraction, electron microscopy, neutron diffraction atomic force microscopy, hyperspectral imaging, FTIR spectroscopy, Raman spectroscopy, LIBS spectroscopy, diffuse reflectance spectroscopy, and/or other high frequency wave-based sensing methods, or photonics or spectroscopic methods.
  • the methods may be used to gather compositional or structural data associated with crystal composition and lattice structures.
  • wave-based sensing devices may include mechanical wave or oscillation-based devices.
  • the devices may include 1-port and 2-port systems, configured, for example, for ultrasonic pulse velocity measurements, mechanical impedance devices, piezoelectric transducer-based devices, optomechanical sensor devices, electromechanical devices, and more.
  • the devices may be used to measure any mechanical property including those listed herein, at one or multiple frequencies.
  • further embodiments of wave-based sensing devices may include electromagnetic wave or oscillation- based sensing devices.
  • sensors may be used to collect data associated with the material in real-time during production, synthesis, and/or use of the material. Additionally, or alternatively, sensors may be used to collect data throughout the material life cycle, monitoring the raw materials, throughout the production process, all the way to the material during use. Further, live contextual conditions such as temperature, pressure, geometry, time or aging, and/or the like may be collected alongside sensing of the material itself. Additionally, or alternatively, contextual conditions may be linked to associated sensor data such as sensor time series data (e.g., the temperature during a particular time, date and/or location,) and/or may be linked to the material sensor data collected at that time, date and location. In some embodiments, contextual condition data may be added to the training dataset or database.
  • sensor data may be used to detect defects or impurities in materials from a production process, and, as a results, one of or a plurality of aspects of the production process may be dynamically adjusted.
  • sensor data may be used as an input for models that predict future expected properties of materials based on historical performance data.
  • models may use multimodal data from a plurality of sensors and sensor types, either during training or model execution.
  • sensors may be used to collect material data at different scales.
  • the data may be used for multiscale analysis and multiscale modeling as part of the models herein.
  • the devices may include thermal sensors, wave-based sensors including piezoelectric sensors, spectroscopy sensors, quantum-based sensors, or any combination of any sensors mentioned.
  • a list of representative and non-exhaustive sensors devices considered by the present disclosure may include quantum-based sensors, hybrid sensors, wave-based sensors, mechanical wave-based sensors, piezoelectric EMI devices, acoustic devices, electromagnetic wave-based sensors, electrochemical devices, magnetochemical devices, electromagnetic wave impedance sensing devices, refractive index sensing devices, ground penetrating radar sensing devices (GPR), terahertz frequencies sensing devices, NMR and/or microwave spectroscopy devices, LIBS spectroscopy devices, FTIR spectroscopy devices, X-ray diffraction devices, hyperspectral imaging devices, LIDAR devices, RADAR devices, temperature sensors, strain gauges, barometers, and/or the like.
  • the server 200 may be configured to control or otherwise influence operations of the one or more sensors device 102a-n and as described hereafter and may be configured to receive from the one or more sensor devices 102a-n datasets comprising data entries associated with various measurements (e.g., measurement types) of a building material. Still further, the server 200 may comprise or be communicably coupled with one or more databases 108. In some embodiments, the system 100 may further include various user devices 106 (e.g., mobile phones, laptop computers, etc.) by which a user associated with the system 100 may interact with the system 100, such as via a user interface of the user device 106.
  • various user devices 106 e.g., mobile phones, laptop computers, etc.
  • the server 200 may be configured to, based upon the data received from the various sensor devices 102a-n and/or databases 108, generate a material identifier associated with a building material, generate sensor context awareness data, generate and/or modify a structural progress flow, and/or generate construction status identifiers as described hereafter.
  • the communication network 104 may be any means including hardware, software, devices, or circuitry that is configured to support the transmission of traffic (e.g., data, signals, etc.) between components of the system 100.
  • the communication network 104 may be comprised of any combination of the above-mentioned protocols.
  • the communication network 104 may include the on-board wiring providing the physical connection between the component devices.
  • the system 100 may include one or more databases 108 configured to store data generated by the server 200, the one or more sensor device 102a-n, or the like.
  • the database(s) 108 may be accessible by the server 200, such as to retrieve data for comparison with data generated by the one or more sensor devices 102a-n.
  • the server 200 and the one or more sensor device 102a-n may, in some embodiments, include common components and/or functionality.
  • the embodiments of the present disclosure are described hereinafter with reference to the server 200 performing the various building material related operations based on data entries generated by the sensor devices 102a-n.
  • the present disclosure contemplates that, in some embodiments, the sensor devices 102a-n may be configured to, in whole or in part, perform the building material operations described herein.
  • each of the devices described herein may include the components necessary to perform one or more of the operations described hereinafter.
  • the system 100 may include any number of intermediary devices communicably coupled within the system 100.
  • the system 100 may include various host devices, gateway devices, etc. that receive data generated by the sensor devices 102a-n and provide this data to the server 200.
  • Example Server Circuitry [00140] With reference to Figure 2, example circuitry components of the server 200 are illustrated that may, alone or in combination with any of the components described herein, be configured to perform the operations described herein with reference to Figures 3-13.
  • the server 200 may include, be associated with or be in communication with processor 202, a memory 206, and a communication interface 204.
  • the processor 202 may be in communication with the memory 206 via a bus for passing information among components of the server 200.
  • the memory 206 may be non-transitory and may include, for example, one or more volatile and/or non-volatile memories.
  • the memory 206 may be an electronic storage device (e.g., a computer readable storage medium) comprising gates configured to store data (e.g., bits) that may be retrievable by a machine (e.g., a computing device like the processing circuitry).
  • the processing circuitry may include one or more processors configured in tandem via the bus to enable independent execution of instructions, pipelining and/or multithreading.
  • the processor 202 may be configured to execute instructions stored in the memory 206 or otherwise accessible to the processor 202.
  • the processing circuitry may be configured to execute hard coded functionality.
  • the processing circuitry may represent an entity (e.g., physically embodied in circuitry) capable of performing operations according to an embodiment of the present disclosure while configured accordingly.
  • the processing circuitry when the processing circuitry is embodied as an ASIC, FPGA or the like, the processing circuitry may be specifically configured hardware for conducting the operations described herein.
  • the processor 202 when the processor 202 is embodied as an executor of instructions, the instructions may specifically configure the processor to perform the algorithms and/or operations described herein when the instructions are executed.
  • the processor 202 may be a processor of a specific device configured to employ an embodiment of the present disclosure by further configuration of the processing circuitry by instructions for performing the algorithms and/or operations described herein.
  • the processor 202 may include, among other things, a clock, an arithmetic logic unit (ALU) and logic gates configured to support operation of the processing circuitry.
  • ALU arithmetic logic unit
  • the communication interface may include a communication modem and/or other hardware/software for supporting communication via cable, digital subscriber line (DSL), universal serial bus (USB) or other mechanisms.
  • the communication interface 204 may provide for communications under various modes or protocols, such as the Internet Protocol (IP) suite (commonly known as TCP/IP). Protocols in the IP suite define end-to-end data handling methods for everything from packetizing, addressing and routing, to receiving. Broken down into layers, the IP suite includes the link layer, containing communication methods for data that remains within a single network segment (link); the Internet layer, providing internetworking between independent networks; the transport layer, handling host-to-host communication; and the application layer, providing process-to-process data exchange for applications.
  • IP Internet Protocol
  • circuitry should be understood broadly to include hardware, in some embodiments, the term “circuitry” may also include software for configuring the hardware.
  • circuitry may include processing circuitry, storage media, network interfaces, input/output devices, and the like, other elements of the server 200 may provide or supplement the functionality of particular circuitry.
  • Example Sensor Device Hardware As an initial matter, the present disclosure contemplates that any embodiment and/or any method described herein, in full or in part, of any device, sensor, actuator, transducer, accessory and/or any other component that may be described herein associated with any device herein may be used in combination to produce another embodiment of the present disclosure.
  • Sensor and node devices may communicate via any number of communications interfaces that may be wired or wireless. They may communicate to other sensor devices, node devices, hub devices, and/or, without loss of generality, any other device type configured to receive communications. In certain cases, the sensor or node devices will not have a direct connection to the internet and will therefore require a hub (or gateway) device, or a personal device (such as a smartphone) to relay the data to other parts of the system.
  • the hub (or gateway) may be a device that may be used to transmit data collected from node devices and/or sensor devices (or data about itself) to the internet, the cloud, a server, and/or any external store of data.
  • any of the aforementioned sensor device hardware may be included in any of the quantum sensing techniques and/or quantum devices as described herein.
  • Advanced RF techniques may also be leveraged by the embodiments described herein.
  • the advanced RF communication techniques may be engineered to optimize data transmission between sensors embedded in, surface mounted on, directed at, or in proximity of the material and other devices or the internet (e.g., a server), and optimizing for reliability, efficiency, and power management in challenging construction environments.
  • the system may use broadband radio frequency sensors and antennas, optimized for minimal signal attenuation and maximal reflection analysis, to enable real-time monitoring and reporting even in dense construction materials.
  • Adaptive impedance tuning may be done by using variable resistors, varactors, variable inductors, or other variable property components (optionally electrically actuated) on the RF front-end.
  • RF Amplifiers may be used to amplify the signal. Additionally, or alternatively, the settings on those RF amplifiers may be modified adaptively based on whether or not the device is embedded in concrete and/or other materials contemplated herein (increasing output power based on the medium surrounding the RF elements).
  • antenna diversity may also be employed. Further, multiple antennas may be spatially distributed. In such embodiments, they may be oriented differently to ensure different polarization of electromagnetic waves and are used for signal generations and detection.
  • antenna arrays may be installed on devices, so as to control direction and polarization of wave propagation. Phased array antennas may be employed for beamforming to direct RF communication towards specific locations (e.g., out of the concrete, away from rebar, and/or the like). Further, adaptive beamforming may also be implemented in some embodiments (based on feedback about success of communication, or other sensors (e.g., S parameter sensing)). MIMO techniques may also be implemented in some embodiments.
  • any of the aforementioned RF techniques may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • Devices may include one or more sensor elements and/or one or more actuator elements of different types. Some techniques may be passive (e.g., only requiring sensor elements), and others may be active, requiring actuator elements (e.g., takes an input signal and generates an output excitation).
  • the sensor element typically measures the material response for analysis (e.g., the response of the element itself, of the material of interest, or any other related element).
  • active techniques may require at least one actuator and one sensor.
  • devices may also employ transducers (e.g., that convert one form of energy into another), and vice versa (e.g., mechanical energy to electrical energy). All actuators (in the general sense of the term) are transducers, but not all sensors are transducers (some sensors, such as the photo elastic elements described herein, exploit changes in their properties caused by their environment, which need independent excitations to be measured).
  • Sensors, actuators, and/or transducers may exploit various physical couplings (e.g., electro-mechanical, electro-chemical, electro-magnetic, electro-thermal, magneto-mechanical, magneto-chemical, magneto-thermal, photo-mechanical, photo-electric, photo-chemical, photo-thermal, and/or third order couplings).
  • This may include any possible combinations of couplings between electric fields, magnetic fields, electromagnetic fields or waves (including optical waves and photonics, but also RF waves), and/or mechanical displacements and waves.
  • the devices that exploit these couplings may be reciprocal (e.g., acting as both actuators and sensors), or non-reciprocal (e.g., only acting as a sensor or actuator).
  • the wave-based sensing aspect of the present disclosure pertains to the generation of, use of, and/or sensing of waves, excitations, or oscillations (e.g., electromagnetic waves and/or mechanical stresses) for the purposes of measuring and characterizing material properties.
  • these modulations may be varied actively over the course of the measurement process and may involve operational modes that may include, but are not limited to, the following: amplitude or power modes (e.g., continuous amplitude or power; periodic amplitude or power; pulsed amplitude or power (where the wave is generated over a discrete time window)); random adjustments to amplitude or power (relating to all of the previous modes); frequency modes (e.g., single frequency emission; harmonic frequency emissions (e.g., multiple, simultaneous single-frequency emissions); frequency sweeps (e.g., time- varying frequency change of single-frequency emission); broadband emission (e.g., wave emission over a range of simultaneous frequencies); random frequency emission (relating to all of the previous modes).
  • amplitude or power modes e.g., continuous amplitude or power; periodic amplitude or power; pulsed amplitude or power (where the wave is generated over a discrete time window)
  • random adjustments to amplitude or power relating to all of the previous modes
  • frequency modes e.g.,
  • temporal-phase modes e.g., pulsed timing; fixed time-delays; sweeped time-delays; random time-delays (relating to all of the previous modes)
  • polarization modes e.g., circular polarization; uni-axial polarization; random polarization
  • position modes e.g., fixed wave sources or wave receivers; moving wave sources or wave receivers; mixed combinations of moving and fixed wave sources or wave receivers
  • wave sources or wave receivers internal to a material wave sources or wave receivers external to a material; mixed combinations of internal and external wave sources or wave receivers; any of the previous in any combination.
  • Wave-based material characterization techniques cover both electromagnetic and mechanical waves as a mechanism to probe underlying material properties.
  • VNA vector network analyzer
  • Another example of a device configuration is the use of high-power, high-frequency electromagnetic radiation in order to vaporize a localized volume of concrete and/or other materials contemplated herein, so as to measure its chemical composition directly via the use of a light-based spectroscopy technique on the resulting plasma, and to therefore provide a material identifier or mix optimization insight with respect to that building material.
  • Another example of a modal operation of the present disclosure is one in which the motion of wave sources and/or wave receivers from within or external to a given material are utilized in order to create a 3D representation of its internal physico-chemical constituents or mechanical properties or of a 3D distribution of some set of contextual conditions (e.g., the spatial distribution of temperature, force-loading, mechanical expansions, or shrinkage).
  • This technique is described as a form of material tomography, that may be used in the broadest sense to measure a measurable material property as a function of spatial position within a material element.
  • any of the aforementioned power management embodiments may be included in any of the quantum sensing techniques and/or quantum devices as described herein.
  • Mechanical Waveguides and Resonators [00166] Mathematically, the concepts developed around waveguides, frames, and resonators of the present disclosure for electromagnetic waves translate naturally to other realms such as mechanical waves. This may be understood from the consideration of the wave equation, allowing for the differences in physical contributions and the boundary conditions.
  • phase velocity of the lowest propagating mode in an acoustic waveguide is generally close to the free-space sound velocity, so sound velocity may conveniently be measured in a waveguide as a function of gas composition, temperature, and pressure, in the presence of a flow field, and even in turbulent flows. Similarly, damping of waves may be a measure of the shear viscosity of the medium. [00167] These ideas may be extended to a solid medium, specifically in the context of wave- based sensors. From a waveguide perspective, typically surface acoustic waves such as Lamb waves are excited.
  • An equivalent electrical circuit may be constructed for mechanical systems and may be formed of wave-based sensors and frames (e.g., acoustic waveguides). This is used to understand and/or tune complex mechanical systems.
  • EIS electrochemical impedance spectroscopy
  • an electrical potential applied across an analyte is used to determine the electrochemical impedance at a plurality of frequencies.
  • the response of a sample to one or more perturbation time-varying excitations may be monitored, and the fraction of energy that may be stored (including stored potential energy (e.g., capacitive, dielectric, or stiffness components and also kinetic or magnetic energy (e.g., inductive or inertial components)) versus the fraction of energy dissipated (e.g., resistive, damping component of impedance, and/or the like) by the sample.
  • stored potential energy e.g., capacitive, dielectric, or stiffness components and also kinetic or magnetic energy (e.g., inductive or inertial components)
  • the fraction of energy dissipated e.g., resistive, damping component of impedance, and/or the like
  • the relaxation time scale (e.g., the time that it takes the sample to return to equilibrium after excitation by the input) may be measured as a function of frequency.
  • the input to generate the excitation may take any of the forms described herein (e.g., sinusoidal, multi-sine, step functions, delta functions, and/or the like), and the measured output impedances may take any of the forms described herein, or any other impedance-like, or impedance analogous measure that may involve other fields, flows, or forces.
  • the measured output of the coupled system is the electromechanical impedance (where the electrical impedance of the electromechanically coupled sensor is measured). It follows that other couplings may be employed (e.g., optomechanical, magnetomechanical, magnetochemical, optochemical, optoelectric, and any other two coupling permutations). Higher order couplings (e.g., 3 couplings, 4 couplings, and so on, such as electro-magneto-mechanical, electro-opto-mechanical) may also be employed in certain embodiments, for example when a second order coupling that is not electronic in nature is measured by an electronic system.
  • the fundamental types of impedances in respect of the physical phenomenon they are related to) are described hereinafter.
  • the coupling When they are measured through a coupling, the coupling is typically prefixed to them, and the measured impedance quantity is labeled after the “coupling type” and “phenomenon domain” (e.g., “electromechanical impedance,” “optomechanical impedance,” and the like).
  • the “coupling type” and “phenomenon domain” e.g., “electromechanical impedance,” “optomechanical impedance,” and the like.
  • the measured output may include mechanical displacements and deformations, mechanical wave characteristics in the host material (e.g., mechanical impedance frequency response, acoustic or elastic impedance frequency response, or, for an N-port system, the S-parameters or S- Matrix and T-Matrix).
  • mechanical wave-based elements that may leverage a number of different physical force couplings as described herein. In some cases, these are built from smart materials.
  • an element can both actuate and sense.
  • different elements are used for actuation and for sensing, which may or may not be spatially collocated, or on the same or distributed across different devices.
  • sensing and actuation are spatially separated, typically the system involves traveling waves, rather than just oscillations.
  • the signal analysis may then be thought of as the determination of the transfer function for the system.
  • a mechanical excitation may be driven through a host material through one or more actuator elements.
  • the response of the material to those mechanical oscillations is then measured using a sensor element. This may take the form of a frequency response analysis (e.g., impedance spectroscopy), intensity response, time response, and/or the like.
  • the input signal that excites the actuation element may take a variety of forms, including waveforms such as delta functions, square waves, step functions, sinusoids, or a sequence of custom pulses constructed from one or multiple oscillatory frequencies. Additionally, or alternatively, the input may be a frequency sweep (e.g., a chirp that may include up-chirping or down-chirping).
  • the input signals are applied to the actuator that then produces mechanical displacements or deformations in the actuating element (e.g., through the applicable coupling). This in turn, creates a displacement and deformations of the host material.
  • any of the aforementioned MAIS techniques may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • Piezoelectric Sensing and Actuation One implementation of electromechanical sensing and actuation makes use of piezoelectric elements.
  • the piezoelectric transducer may excite a volume surrounding it (where the size of the volume of influence is related to the size of the piezo element, and the power / energy input and the material properties).
  • Piezoelectric elements may also be stacked for increased effectiveness, and importantly, enabling custom piezo active shapes which can enable various resonance modes (through irregular piezo active elements).
  • any of the aforementioned piezoelectric sensing and actuation techniques may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • E&M Wave-Based Sensing Devices Materials properties may also be characterized based on the impact of time-varying electromagnetic fields on material properties (actuation), and the impact of the material on those electromagnetic fields (sensing).
  • the present disclosure categorizes electromagnetic wave-based sensing techniques along the frequency domain as follows. Low frequency excitation and sensing includes from zero up to the frequencies where time-varying electric and magnetic fields begin to exhibit wave propagation in the medium.
  • the frequency at which this occurs will depend on the material under consideration including its permittivity, permeabilities and conductivities. For practical purposes, these are defined dynamically as the frequency where electromagnetic waves begin to propagate within the medium with an attenuation of less than 1/e. At these low frequencies, wave propagation is not dominant due to their interaction with dipole moments and the like.
  • this band may be split into electrochemical and magnetochemical depending on whether electrical or magnetic fields are predominantly driven by the actuator or drive the sensor’s response. Electro-magneto-chemical couplings may also be employed.
  • Mid frequency excitation and sensing includes electric and magnetic fields beginning to exhibit a tightly coupling interaction, allowing electromagnetic waves to propagate effectively in the medium.
  • High frequency excitation and sensing includes electric and magnetic fields beginning to interact with molecules and atoms in the media, that begin to impede wave propagation.
  • the frequency band starts somewhere in the infrared spectrum and includes any frequency beyond it (e.g., infrared, visible, ultraviolet, x-rays, and gamma rays). At these frequencies, due to the lower wavelength and higher energy, interaction with the material happens at the atomic or particle level, that leads to different techniques.
  • any of the aforementioned E&M wase-based sensing devices may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • Electromagnetic Wave Impedance In electromagnetic wave impedance sensing mode, the methods described herein may make use of one or more broad spectrum radio frequency sensors or detectors and one or more antennas. In active monitoring configurations, one or more antennas may be used to generate signals at different frequencies and locations within a host material so as to be able to monitor signal attenuation, reflection, electromagnetic wave impedance, and general frequency response. These parameters may be monitored over time as the material evolves, but also over space to determine their spatial distribution.
  • any of the aforementioned embodiments directed to electromagnetic wave impedance may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • Refractive Index and Polarization Sensing [00198] Using specialized optics and/or antennas, reflective real and imaginary components of the refractive index (e.g., attenuation and speed of light change and in parallel light polarization) and polarization of electromagnetic waves (e.g., described in the form of Jones matrices or Mueller matrices) may be determined, from both within as well as from the surface of host materials (including the boundary effects of the surface, as well as the impact of the medium itself on wave propagation).
  • the refractive index e.g., attenuation and speed of light change and in parallel light polarization
  • polarization of electromagnetic waves e.g., described in the form of Jones matrices or Mueller matrices
  • refractive index sensing may be related back to electromagnetic wave impedance.
  • electromagnetic waves may be directed onto or into a material at one or more angles, and the transmitted wave’s intensity and beam deviation may be measured to calculate the refractive index.
  • RF or optical waveguides may be used with a direct, angled boundary into the medium, to aid in refractive index characterization.
  • refractive index sensing involves measuring the intensity and angular deviation of electromagentic waves as they interact with the material. By analyzing these changes, the system may detect variations in the material's refractive index. Determination of the refractive index would in turn allow for monitoring of the material’s relative permittivity and permeability in the chosen frequency range.
  • multiple polarized antennas, or optical analyzers may be used to sense the polarization (or change in polarization) of electromagnetic waves within a medium.
  • a uniformly polarized excitation signal may be generated (e.g., the excitation signal may be circularly polarized, linearly polarized, or elliptically polarized).
  • At least two (and optionally three, for three-dimensional sensing) perpendicular polarized antennas may be disposed in the path of the electromagnetic wave propagation.
  • the two perpendicular antennas may be used as an analyzer, to fully characterize the x and y components of the wave’s polarization. With three antennas, the direction of propagation, and polarization may all be determined. It is worth noting that in the mechanical world an analogue exists for the detection of the mode of the wave (e.g., transverse waves, longitudinal waves, surface waves, and/or the like), and in the case where the waves are transverse or surface-based, their polarization may be characterized similarly. [00201] The technology is applicable across various frequency ranges, including mid and high frequencies (e.g., the visible light spectrum).
  • the mid-frequency electromagnetic regime (e.g., RF, microwave, and/or the like) allows for good wave propagation through the medium, making refractive index and polarization sensing particularly advantageous in this part of the spectrum.
  • optical domain waves will attenuate too fast in the medium.
  • waves may propagate in other materials which may be coupled to the host material (e.g., a photonic waveguide or photoelastic materials).
  • any of the aforementioned embodiments directed to refractive index and/or polarization sensing may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • NMR, EPR, and/or Microwave Spectroscopy Nuclear magnetic resonances, electron paramagnetic resonances, and microwave spectroscopy techniques may be used in the radio and microwave part of the spectrum to produce a response spectrum. In the case of NMR and EPR, this is related to the spin of their nuclei and electrons respectively. Spectroscopic analysis may then be carried out to determine compositional properties of the sample over time (e.g., signals that demonstrate absorption due to water and its decaying influence as concrete cures and dries).
  • ultra- low-power, low-cost wireless, miniaturized devices are designed with NMR, EPR, and microwave spectroscopy capability for field material characterization and identification.
  • NMR nuclear magnetic resonance
  • EPR electrospray spectroscopy capability
  • microwave spectroscopy capability for field material characterization and identification.
  • the magnetic moments of those particles align with the applied field, but they also process around the field direction at a frequency known as the Larmor frequency. This frequency is different for each type of nucleus and also depends on the applied magnetic field strength.
  • NMR samples are placed in a magnetic field, and radiofrequency pulses are used to perturb the magnetic moment alignment and precession frequency. The nuclei absorb energy from these pulses and move into a higher energy state.
  • NMR, EPR, and, more broadly, microwave spectroscopy is highly effective in monitoring material characteristics (e.g., curing (in particular water changes)). For example, as concrete cures, changes in the concrete properties are tracked over time to assess the curing stage and overall quality of the concrete and to make determinations about the water to cement ratio and the compressive strength of the concrete, as well as its setting time.
  • material characteristics e.g., curing (in particular water changes)
  • curing in particular water changes
  • changes in the concrete properties are tracked over time to assess the curing stage and overall quality of the concrete and to make determinations about the water to cement ratio and the compressive strength of the concrete, as well as its setting time.
  • the system houses specialized dual-mode generators capable of emitting both NMR (radio) and EPR or microwave frequencies. Precision control mechanisms are integrated to ensure the stability and accuracy of the frequencies generated.
  • any of the aforementioned NMR, EPR, and/or microwave spectroscopy techniques may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • High-Frequency near-IR, visible, UV, X-Ray+
  • High frequency wave-based sensing techniques involve electromagnetic waves (sometimes colloquially referred to as “light”) with frequencies at the near-IR band and upwards. At these frequencies, electromagnetic waves carry enough energy to begin exciting atoms and particles. These excitations and interactions are used to characterize the medium under consideration, from its fundamental constituents upwards.
  • Intensity spectroscopy and imaging sensing may refer to a class of sensing techniques that include analyzing the electromagnetic intensity-frequency spectra of electromagnetic waves emitted, absorbed, reflected, transmitted, and/or otherwise interacted with or radiated by a medium.
  • Photonic sensing may refer to a class of sensing techniques that use materials with photonic properties that may control and/or influence electromagnetic waves in or around the IR, visible, and/or UV spectrum to engineer conditions that are particularly advantageous for sensing the interaction of these electromagnetic waves with the material under consideration (e.g., host medium or material).
  • the sensor devices disclosed herein may principally be used to measure compositional, contextual, and/or static material properties of the host material, as well as material and/or device contextual conditions, and/or any other data type described herein. Any part or sub-part of any embodiments, disclosures, and/or further descriptions herein, may be used to enable, in full or in part, any method described herein. Any part or sub-part of any embodiments, disclosures and/or further descriptions herein may also, without loss of generality, be used for any other embodiment of any sensor device described herein.
  • any of the methods described in this section may be used on any hardware embodiment disclosed (e.g., low-cost mobile battery powered field devices designed to be embedded and/or attached within materials, able to communicate wirelessly using any of the communication methods described herein and coupled with smartphones and cloud-based machine learning models for analysis). These low-cost devices of the present disclosure present a step-change away from bulky lab-based spectroscopy that are incapable of performance in the field as described herein. [00215] As will be understood by one or ordinary skill in the art in view of the present disclosure, any of the aforementioned high frequency wave-based sensing techniques may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • Intensity Spectroscopy and Imaging Sensing may present a way of measuring the data types (e.g., any material property) described herein, and, in particular, for the determination of compositional material properties (e.g., atomic elements, compounds, formulations, and/or the like), as well as any contextual and/or static material properties associated with the chemistry, atomic structure, and/or other atomic level properties of the material.
  • the sensor devices described herein may also present methods of measuring other types of properties of matter. In this way, the device embodiments listed herein may be particularly useful for intrinsic material identification purposes.
  • Said devices therefore present a highly novel and accurate material monitoring tool based on fundamental, atomic-level chemical and physical material properties, that may self-identify materials.
  • imaging techniques allow for spectral electromagnetic wave tomography (e.g., as electromagnetic spectra are spatially mapped to different areas of materials, comprehensive characterization of the materials may be made).
  • spectral electromagnetic wave tomography e.g., as electromagnetic spectra are spatially mapped to different areas of materials, comprehensive characterization of the materials may be made.
  • These techniques may be applied to cementitious mixes and/or concrete mixes or any of their raw materials, but also other materials used in construction such as steel beams and/or rebar, timber, coatings such as intumescent paint, and without loss of generality any building materials, composite material, raw material, mined or extracted material and/or other materials described herein.
  • any of the aforementioned intensity spectroscopy and/or imaging sensing techniques may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • LIBS Spectroscopy Sensing Device [00218] A LIBS Spectroscopy embodiment of the electromagnetic high-frequency wave- based sensing devices may make use of LIBS spectroscopy for material property determinations. LIBS Spectroscopy may include a method using high intensity lasers to transfer energy into and excite a microscopic volume of material into a state of plasma for a very short time interval.
  • this ‘micro-plasma’ de-excites, it emits radiation corresponding to the spectral energy levels of its component molecules. This is detected by a spectrometer or other electromagnetic wave detection device.
  • the process, from electromagnetic wave emission to detection may last a few hundred nanoseconds and may be considered a non-destructive or quasi-non-destructive technique in material applications, since negligible samples of material are converted into plasma and as such, the structural integrity of the material is not compromised.
  • the spectrometer reconstructs the material’s intensity-frequency spectrum based on the received electromagnetic waves. Using this spectral data, attributes and/or properties of the material under consideration may be determined.
  • properties of interest for determination may include material compositional properties, including material formulation and/or raw material concentration within the material, as well as contextual material properties including compressive strength, and other properties associated with the rate of hydration in early-age cementitious mixes.
  • the LIBS device may be configured to do spectral analysis for material compressive strength determination in cementitious mixes.
  • One analysis method for execution of this determination includes detecting the intensities of the dominant Calcium I & Calcium II spectral lines, known to exist at 422.6nm for Ca I, and at 393.3nm and 396.8nm for Ca II. Once these are detected, the ratio between the intensity of the Ca I & Ca II (either Ca II lines) may be correlated to the compressive strength of concrete.
  • Calcium compounds comprise many of the reagent compounds in the hydration reaction of cementitious mixes and may be used for compressive strength determinations that strongly correlate with the hydration reaction.
  • the relationship between the intensity ratio and the compressive strength of the cementitious mix may be linear.
  • spectral analysis includes a calibration step.
  • Enhanced LIBS methods may employ double pulse excitation, spatial configuration, magnetic confinement, spark discharge confinement, or DFLS to improve measurement accuracy.
  • any of the aforementioned LIBS spectroscopy embodiments may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • FTIR FTIR Spectroscopy Sensing Device
  • FTIR measures absorption of infrared light, providing an absorption spectrum displaying the frequencies at which a sample absorbs incident photons. A sample is illuminated with IR light, and absorbed light energy is converted into defined molecular vibrations.
  • FTIR covers a wider spectral range, typically from the near-infrared to the far-infrared region. The technique may be particularly advantageous for material identification, in that it can provide information about molecular vibrations, including functional groups and chemical bonding. This means it may be used to complement other methods such as LIBS that provide elemental data.
  • Each functional group in a molecule has characteristic unique vibrations that are reflected at different bands in the infrared spectrum.
  • Individual bands in an infrared spectrum may be used to determine what functional groups are present in a sample.
  • the bands of all these different functional groups together result in a Fourier transform infrared (FTIR) spectrum that may be considered a material identifier of the sample.
  • FTIR Fourier transform infrared
  • This technique may be particularly useful for mix fingerprinting applications.
  • the region in which most of the characteristic vibrations are present is called the fingerprint region.
  • the fingerprint region is located at the lower end of the so-called mid-IR region. Infrared spectroscopy requires light from the mid-IR region, which spans from about 4000 to 400 cm -1 .
  • any of the aforementioned FTIR spectroscopy sensing devices may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • Hyperspectral Imaging Sensing Device Another significant element considered in some embodiments includes the use of hyperspectral imaging to characterize the fresh, hardened, and/or other properties of materials (e.g., concrete), in both 2D and 3D. Material property determinations may include, for example, compressive strength, water to cement ratio, and the like.
  • ultra-low cost hyperspectral devices may be built, enabling much wider adoption of embedded, surface mounted or near- pour devices.
  • a hyperspectral camera By directing a hyperspectral camera at a material element (e.g., by mounting it near the surface), the full spatially distributed electromagnetic spectrum for each pixel (which represents an area of material dA) may be mapped, providing invaluable information on its chemical composition.
  • Higher resolution hyperspectral imaging is also able to build up a distribution of material within the material (aggregate, material matrix, and/or the like). This allows for clustering of spectra for the different subcomponents of a material mix, enabling the characterization of the material matrix and the aggregate type.
  • an illumination source may be used to illuminate the materials described herein. Additionally, or alternatively, the illumination source may be a broadband light source or narrowband light source. Additionally, or alternatively, there may be one single light source or a plurality of light sources. Additionally, or alternatively, the light source may be LED based. Additionally, or alternatively, the hyperspectral imager may employ a tunable filter to compose a hyperspectral cube (e.g., a MOEMS-based tunable Fabry Perot filter).
  • a tunable filter to compose a hyperspectral cube (e.g., a MOEMS-based tunable Fabry Perot filter).
  • a plurality of narrowband illumination sources may be turned on and off in succession at a predefined pattern, and the output recorded by the camera, and processed to create a hyperspectral cube.
  • the subcomponents of the material e.g., aggregate vs material matrix
  • this output may then be used to construct one or more distinct frequency spectra for the whole or parts of the material matrix, and/or the aggregate, and/or any other parts of the material.
  • similar substructure may be identified, separated, and characterized.
  • any of the aforementioned hyperspectral imaging sensing devices may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • Raman Spectroscopy Sensing Device [00229] Raman spectroscopy involves the use of laser light to interact with molecular vibrations, phonons, or other excitations in a material. Said technique provides detailed information on molecular composition, crystal structure, and other physical properties. It may be used to study a materials crystalline and/or amorphous form.
  • Raman spectroscopy may be employed to characterize materials (e.g., concrete) in lab environments, but Raman spectroscopy may require specialist, costly equipment, and may require samples to be sent from the field to the lab.
  • an ultra-low-power, battery powered or energy harvesting device designed to either be surface mounted, or embedded into materials, and wirelessly communicate with other devices or the internet or the cloud, may be configured to carry out Raman Spectroscopy in the field, a feature not found in conventional Rama based systems.
  • all of the device hardware features described herein may be incorporated into such devices, and spectroscopy techniques described for other embodiments herein may be transferred to this embodiment (including adaptive optics, movement and guiding of beams and/or the like).
  • a monochromatic laser e.g., a diode laser
  • Optical fibers and/or lenses guide the laser beam to a surface area of concrete.
  • a trapezoid prism-shaped housing, and/or a semi-parabolic convex dome design may be employed to support precise directing and focusing of the laser beam onto the surface of the material of interest.
  • the device detects Raleigh scattering, Stokes-Raman scattering, and/or anti-stokes Rama scattering to determine vibrational modes of molecules in the material of interest (e.g., concrete), to identify them.
  • the source light may be produced by a single or multiple lasers across one or a wide variety of wavelengths.
  • a high-resolution spectrometer is used to analyze the scattered light, and separate the Raman scattered light into its constituent wavelengths. This may take the form of a CCD or CMOS sensor alongside a Fabry-Perot Tunable Filter (or any other spectroscopy technique described herein).
  • any of the aforementioned Raman spectroscopy sensing devices may include any of the quantum sensing techniques and/or quantum devices as described herein.
  • XRD Spectroscopy Sensing Device [00233]
  • the X-ray part of the spectrum can be treated as an extension of the visual spectrum and as such X-rays can be used to both excite the unit or material under test as well as monitor its absorption and reflection of said source X-rays. This enables the determination of material composition and other material properties, including their changes over time and space.
  • XRD spectroscopy is employed to characterize concrete or cement in the lab, but it requires specialist, costly equipment, and importantly, requires samples to be sent from the field to the lab.
  • the device may be configured to execute differential reflectance spectroscopy.
  • DRS spectroscopy may involve characterization of the difference in intensity as well reflectance spectra of the surface of the material under consideration with respect to different parameters, for example changing wavelengths.
  • Light may probe the surface of the material and be reflected back towards a photodetector.
  • the reflectance spectrum may comprise values for the surface differential reflectivity for a plurality of frequencies, optionally a continuum of frequencies (frequency band).
  • the surface differential reflectivity may be defined according to a 3-surface model. [00237] In one embodiment, this may be used to characterize the dielectric properties of the surface of the material.
  • the temperature sensors may further be configured to enable maturity sensing in cementitious mix related applications.
  • Thermal Tails including MPTTs
  • MPTTs may be used for spatial temperature characterization of the material.
  • Point sensors such as temperature sensors (e.g., maturity sensors) may be used in combination with any other sensor type described herein.
  • the combination of a temperature sensor and at least one other sensing or actuation method is considered to form the basis for some embodiments of enhanced maturity methods.
  • Quantum sensors of the present disclosure may include at least one quantum gate in the operation of the quantum sensors, and, in more advances cases, may employ one or more quantum processing units (QPUs) in their operation.
  • QPUs quantum processing units
  • the output of said quantum sensors may be classical and/or quantum in nature, the transfer of which may be facilitated by one or more classical channels and/or quantum channels.
  • the sensing techniques described herein may be quantum enabled, where a quantum phenomenon and/or quantum state is used, but the output may be measured via classical sensing devices and/or circuits (e.g., photodetectors in NV centre diamond).
  • the sensing techniques described herein may be fully quantum, where a quantum phenomenon and/or quantum state is used and the output a quantum state.
  • the output may be used alongside other quantum states and may interact with a quantum gate, with the interaction potentially being measured at a later time.
  • quantum channels such embodiments may transfer a quantum state via the quantum channels and may interact the quantum state with other quantum measurements.
  • the quantum state may be prepared in a predetermined state (e.g., preparing a squeezed light state).
  • Embodiments of the present disclosure may be configured in a sensing system than employs more than a single sensor device.
  • a plurality of sensors may be employed and may be operably coupled to each other and/or other elements of the system (e.g., a multi-variate system).
  • the system may be a hybrid system, wherein at least one classical device is used and at least one quantum device is used.
  • systems contemplated by the present disclosure may include multiple quantum sensors.
  • the quantum devices herein may be configured in a portable, mobile device form.
  • the devices may be configured as a classically wireless quantum sensor, a battery powered quantum sensor, a quantumly communicating quantum sensor (may be wireless), and/or a hybrid of the aforementioned.
  • the quantum sensors may measure the gradient (e.g., the ⁇ operator) of fields (e.g., act as a gradiometer).
  • the quantum sensors configured to measure fields may measure fields at point locations.
  • spatial mappings of fields may be generated by arrays of quantum sensors and/or moving quantum sensors.
  • time evolution of fields may be generated.
  • frequency dependent measurements of the field may be generated, Fourier transforms may be performed at different field locations, and/or the like (e.g., quantum impedance spectroscopy (‘MAIS’)).
  • MAIS quantum impedance spectroscopy
  • the quantum sensors herein may measure composition and structure or disposition in space.
  • Such embodiments may include measuring atomic or molecular compositions (e.g., hyper-resolution spectroscopy), types of grains or composites and their disposition (e.g., electric field mapping, magnetic field mapping, or G field mapping), measuring material structure or disposition in space (e.g., detecting particles and their locations, atoms, molecules, and probability density functions, and/or grains, composites, and higher order structure), spatial distribution, time evolution, and/or the like.
  • atomic or molecular compositions e.g., hyper-resolution spectroscopy
  • types of grains or composites and their disposition e.g., electric field mapping, magnetic field mapping, or G field mapping
  • measuring material structure or disposition in space e.g., detecting particles and their locations, atoms, molecules, and probability density functions, and/or grains, composites, and higher order structure
  • spatial distribution time evolution, and/or the like.
  • determination of the fields may include their time evolution at a plurality of locations within a bound volume of material.
  • the measured fields may be a scalar, vector, or tensor timeseries.
  • fields may be real or may be complex.
  • a non-exhaustive list of properties of fields contemplated by the present disclosure includes, electromagnetic properties, thermal properties, gravitational and mass density properties, mechanical properties, chemical properties, optical properties, acoustic and wave properties, energy and transport properties, advanced material properties, structural integrity properties, multi-physics coupling fields, quantum and atomic fields, and/or other specialist properties.
  • a list of electromagnetic properties may include, but is not limited to, B (magnetic field, vector, describes the magnetic force per unit charge); H (magnetic field intensity, vector, describes the response of the material to a magnetic field); E (electric field, vector, describes the electric force per unit charge); D (electric displacement field, vector, accounts for free and bound charge in a medium); A (magnetic vector potential, vector, potential linked to B); ⁇ ⁇ (electric potential, scalar, scalar potential associated with E); P (polarization field, vector, electric dipole moments per unit volume); M ( magnetization field, vector, magnetic dipole moments per unit volume); S (Poynting vector, vector, energy flux in electromagnetic waves); ⁇ (magnetic permeability field, scalar or tensor, describes the ability of a material to support a magnetic field); ⁇ (electric permittivity field, scalar or tensor, describes the ability of a material to permit an electric field); ⁇ ⁇
  • a list of acoustic and wave properties may include, but is not limited to, p (acoustic pressure field, scalar, pressure variations in sound waves); ⁇ ⁇ (velocity potential, scalar, linked to fluid motion in waves); and A (wave amplitude, scalar, intensity of a wave)
  • a list of energy and transport properties may include, but is not limited to, u (energy density, scalar, energy per unit volume); and F (flux fields, vector, transport of mass, charge, or energy).
  • a list of advanced material properties may include, but is not limited to, ⁇ ⁇ (phase field, scalar, phase transitions); k (permeability, scalar, flow through porous media); and order parameters (scalar or vector, state representation, such as ferroelectric domains).
  • a list of structural integrity properties may include, but is not limited to, ⁇ ⁇ (crack field, scalar, location/density of cracks); and ⁇ ⁇ (residual stress tensor, second-order tensor, locked-in stresses).
  • a list of quantum and atomic fields may include, but is not limited to, ⁇ (wavefunction field, complex scalar, quantum state of electrons in a material); ⁇ ⁇ (electron density field, scalar, spatial distribution of electron probability); and ⁇ ⁇ (effective potential field, scalar, potential felt by particles or waves in a material).
  • Examples of derived fields with physical significance and that may be measured or determined for material targets of the systems, devices and methods herein may include, but are not limited to, electromagnetic fields (e.g., ⁇ E (charge density, scalar, describes sources of electric field); ⁇ E (electromotive force, vector, linked to changing magnetic fields); ⁇ B (magnetic monopole density, scalar, usually zero in nature); and ⁇ H (current density, vector, describes sources of magnetic field)), thermal fields (e.g., T (temperature gradient, vector, rate of temperature change); and ⁇ q (heat generation, scalar, net heat production in a region)), mechanical fields (e.g., ⁇ (stress divergence, vector, net force density in a material); and ⁇ u (Vorticity, vector, local rotational deformation)), and fluid dynamics (e.g., ⁇ v (flow divergence, scalar, describes compression/expansion); and ⁇ v (vorticity, vector
  • the one or more quantum sensing elements are based on confined quantum states.
  • said confined quantum states may be spin-defect based, and may include, but are not limited to, nitrogen-vacancy (NV) centers in diamond, silicon vacancies in silicon carbide (SiC), divacancies in silicon carbide (SiC), boron vacancies in hexagonal boron nitride (h-BN), phosphorus donors in silicon, transition metal defects (e.g., Cr, Mn, Ni in Al2O3, MgO), color centers in zinc oxide (ZnO), color centers in gallium nitride (GaN), organic spin defects (e.g., nitroxide radicals), and rare-earth ion dopants (e.g., Er3+ in Y2SiO5).
  • NV nitrogen-vacancy
  • SiC silicon vacancies in silicon carbide
  • SiC divacancies in silicon carbide
  • h-BN boron vacancies in hexagonal boron nitride
  • Quantum dots are nanoscale particles, based on semiconductors and are able to confine electrons in all three spatial dimensions to create discrete quantum energy levels.
  • the electron confinement is caused by the small size of the quantum dot. This enables tunable electronic and optical properties by adjusting the size, shape, and material composition of the quantum dots that, in turn, directly affect the quantum confinement effect.
  • the quantum dots are first prepared prior to use.
  • Such embodiments may include optical excitation to excite electrics in the quantum dot from the valence band to discrete energy levels in the conduction band (e.g., creating an exciton), or electrical control (e.g., through the application of a voltage to nearby gates), and/or spin initialization (e.g., by using polarized light or microwave fields to directly initialize the spin state).
  • measurement of quantum dots is based on the measurement or analysis of the electromagnetic waves emitted by the quantum dots. This analysis may include measuring or analyzing their intensity, wavelength, polarization, and/or the like to reveal information about the energy state of the quantum dot. Additionally, or alternatively, current flow through or in proximity to the quantum dot may indicate its charge or spin state.
  • the quantum state may be transferred to a non-confined state (e.g., a photon), and the full quantum state may then be transmitted on a quantum channel, and/or input into a quantum circuit that may include one or more quantum gates, and/or input and/or processed by a quantum processing unit.
  • the transferred quantum state may interact with other quantum states.
  • Quantum Spatial Mapping of Materials With Arrays [00274] To enable mapping of a target material and/or field, an array of sensors, a moving sensor, or a combination thereof may be employed.
  • multiple planar grids of NV centers may be stacked within the diamond volume on top of one another (e.g., to create a 3D grid). Additionally, or alternatively, multiple distinct diamonds, each with an associated NV center, may be interconnected across a scaffold material or a grid.
  • quantum dot arrays may be used (e.g., for magnetic fields - measure Zeeman Splitting, or spin state transitions).
  • a quantum dot array may be disposed on a substrate with a read-out layer.
  • quantum dots may be embedded or patterned within a solid matrix (e.g., transparent polymer, silica gel, glass, and/or the like).
  • the solid matrix may need to be optically transparent or electrically accessible so a read-out may be obtained.
  • a light source may excite the quantum dots (e.g., laser).
  • a 3D imaging system may capture fluorescence from different depths in volume and electrodes embedded in the matrix may capture responses from the different dots at different points.
  • quantum dots may be at slight offsets to on another to facilitate reading of them all simultaneously.
  • each quantum dot may be paired with a miniature detector and may all wired together.
  • traces may be formed within the substrate to connect each quantum dot with a local photodetector.
  • layers or planes of quantum dot arrays may be within a transparent material with a substrate (e.g., PCB with traces, and read-out points underneath each quantum dot).
  • Density of the array e.g., 2D or 3D array
  • Spacing in the array may be uniform, or it may be variable depending on the desired sensitivity and the application.
  • relative position of each sensor may be known by the CPU, GPU, QPU, or other processing unit so as to reconstruct the map.
  • the array becomes ‘camera-like’.
  • spatial maps of field strengths may be done based on the measurements of each quantum state in the array, and the distances between each quantum sensing elements d (e.g. the distance between neighboring elements). This may include the distance vector (made up of the magnitude and direction between one quantum element and the other), or the scalar Euclidean distance or Manhattan distance. Such distances may be pre-determined during the fabrication of the array and stored / retrieved in our systems or updated during a pre-calibration step prior to the use of the array, determined after the use of the array, or determined dynamically by the array itself or related systems before/during or after the measurement.
  • Embodiments of the present disclosure contemplate a plurality of sensor configurations, sensor movement, and/or sensor geometries.
  • a non -exhaustive list of sensory configurations and movement of sensors configurations may include a single sensor that moves; a 1D, 2D, or 3D array that is fixed (e.g., line or grid); a 1D, 2D, or 3D array that moves in the z direction (e.g., towards and away from the material); a 1D, 2D, or 3D array (x-y-z) that may move in at least one of those directions (or two or all three); a 1D, 2D, or 3D array that may move in any direction represented by a three dimensional displacement vector dv; and a 1D, 2D, or 3D array that may move along either of the three Euler angles.
  • a non-exhaustive list of geometries of arrays may include straight line, square, cuboid, circumference of a circle, circle, sphere, curves, rings, concentric spheres, spirals, various surfaces (e.g., paraboloid), and/or the like.
  • an array may be disposed on a container (e.g., a frame). Further, the array may be embedded, and a material may be poured such that the material fills the container with the array enveloping it.
  • any particular array configuration, or excitation described for one type of spin-defect sensor or other quantum sensor may be applied and/or generalized to other quantum sensor types (including but not limited to other spin-defect types or contained spin state).
  • Quantum Dots [00319]
  • the quantum sensor elements described herein may be based on quantum dots. These are semiconductor nanoparticles that may range from 2 to 10 nanometers in size. Due to quantum confinement, they exhibit size-dependent optical and electronic properties, such as tunable photoluminescence. Quantum confinement arises because the quantum dot size is smaller than the exciton Bohr radius, which leads to discrete energy levels.
  • the system and associated methods may output the complex impedance, its magnitude or its phase or other related quantities. Additionally or alternatively, the system may determine the position of one or more resonance peak within the impedance signals. Additionally or alternatively, the determination may be based on the derivative of the complex impedance, or the application of any other operator onto the impedance measurement. Additionally or alternatively, exotic quantum states (such as entangled states or squeezed states) may be prepared and used by the system to obtain measurements beyond the classical noise or resolution limits.
  • the quantum sensor and/or the actuator may be positioned or disposed in proximity of a frame, or within of a frame to enhance resonance signals. Additionally or alternatively, the exotic state of light may be adjusted adaptively based on prior measurements of the system. Additionally or alternatively, the system may include a plurality of quantum sensors and/or actuators. Additionally or alternatively, the use of arrays (as disclosed in the present disclosure) or dynamic sensors, or continuous field sensors may enable the mapping of the impedance at a plurality of locations. Additionally or alternatively, a spatial map of impedance within the target material may be reconstructed from a plurality of quantum sensors within an array. Additionally or alternatively, the excitation signal generated by an actuator may be spatially directed through the use of actuator arrays and beamforming techniques.
  • the electromagnetic waves may be directed to a specific location within or on the target material using the array and the associated response then measured.
  • the excitation signal may take various forms (e.g. time-varying, uniform in space etc.) through control of the array.
  • Quantum sensors enable higher sensitivity and are able to detect weaker signals. Disposing them in arrays enables spatially distributed measurements and spatial reconstruction of fields, impedance measurements, and material characteristics. They also facilitate the mapping of impedance and other material properties at smaller spatial resolutions, such as variations within a sample at the microscopic level.
  • the quantum system and associated methods are configured for electrochemical impedance measurements.
  • the system employs classical actuators that take the form of one or more electrodes and are in direct contact with the target material.
  • the system employs one moving electrode.
  • the system employs two, three or four electrodes.
  • the system employs interdigitated electrodes. These electrodes apply a time-varying electrical potential across a region or volume within the sample which as a result are able to induce electrochemical reactions and charge movements within the material.
  • Quantum sensors such as those described in the present disclosure (e.g.
  • the one or more quantum sensors may measure the local electric potentials and currents, enabling measurement of the electrochemical reactions occurring at the electrode-sample interface (and if using arrays, at other locations within the material, for example between the electrodes). Additionally or alternatively, this may include analyzing the phase shifts and amplitudes of the measured signals, through which the system derives the complex impedance characteristics, which may include resistive, capacitive, and inductive components, as functions of frequency. Additionally or alternatively, the one or more electrodes may be integrated to sensor arrays, and fully embedded in the material, or placed on the surface of the material. [00341] In another embodiment, a non-contact method utilizes classical actuators which are configured to generate time-varying electric and magnetic fields externally to the target material.
  • Quantum sensors are positioned outside the target material (e.g. disposed in the various array configurations described herein) and used to detect the induced electric (E-field) and magnetic (B-field) responses. Additionally or alternatively, the quantum sensors may be disposed in an array, and be able to capture the target material's response to the external field with high spatial resolution at a plurality of locations in time and a plurality of times. By scanning the fields at various locations, frequencies and times, the system may construct a spatial map of the electrochemical impedance of the target material.
  • an actuator applies a time-varying field to a material sample (which may be gravitational in nature, e.g. through the movement of a mass).
  • the response of the material to the applied field is measured, based on the properties or characteristics of gravitational fields (and related quantities) surrounding the material. These are measured using a quantum gravimeter or gradiometers to measure a mass induced impedance change across multiple frequencies.
  • a quantum gravimeter or gradiometers to measure a mass induced impedance change across multiple frequencies.
  • the methods herein may include the use of quantum spectroscopy systems, including such systems that employ quantum states to achieve spectral super-resolution and/or noise reduction under the shot noise limit. Additionally or alternatively, the methods herein may include preparing a quantum state of light including a squeezed state of light and/or an entangled state of light / photons. Additionally or alternatively, state preparation may include shining a laser through a nonlinear optical crystal. Additionally or alternatively, state preparation may include the use of squeezing cavities to generate squeezed states of light. Additionally or alternatively, state preparation may include parametric down conversion and/or spontaneous parametric down conversion for photon entanglement.
  • the exotic light state e.g. squeezed state and/or entangled state
  • the detection method may include the use of homodyne and/or heterodyne and/or single photon detectors.
  • the quantum light states may be passed through an interferometer system, configured to separate the quantum light state paths, such that one basis state interacts with the target material, and the other does not interact with the target material, reflect each state back towards the detector, recombine the state, and detect the interference pattern generated by the combined quantum states.
  • the spectrum may be used to determine the presence of a chemical compound, molecule, atom, grain and/or the like within the material.
  • the methods herein may include determining a phase shift encoded in the quantum light state by measuring the photon intensity and relating the photon intensity back to the phase shift, wherein the intensity may be associated to a probability and/or probability distribution associated with a quantum basis state. Additionally or alternatively the methods and/or systems herein may include measuring an interference pattern wherein the fringes in the interference pattern may be resolvable beyond the standard limit, and/or determining on or a plurality of light spectra in whole or in part based on the interference pattern.
  • the quantum enhanced spectroscopy methods herein may include in whole or in part preparing an exotic state of light, shining that state of light at the target material, such that the quantum state of light interacts with the material sample and/or is scattered by the target material, measuring the quantum light state following scattering, and/or determining, based on the measurement executed on the quantum light state a spectrum including an intensity-frequency spectrum, wherein the spectrum may be indicative of the species of materials, compounds, molecules, atoms and the like in the target material.
  • preparing the exotic state may include preparing entangled, squeezed and/or single photon states of light.
  • state preparation may include preparing a squeezed state of light in whole or in part by using an optical parametric amplification procedure, wherein a laser beam is shone onto a nonlinear crystal (e.g. KTB, BBO), and/or four-wave mixing procedure. Additionally or alternatively, state preparation may include preparing an entangled state of light and/or entangled photon pairs (or more than 2 photons), for example using Spontaneous Parametric Down-Conversion, wherein this may include interacting light from a laser with a non-linear crystal. Additionally or alternatively, the quantum light state may be configured to interact with the target material.
  • an optical parametric amplification procedure wherein a laser beam is shone onto a nonlinear crystal (e.g. KTB, BBO), and/or four-wave mixing procedure.
  • state preparation may include preparing an entangled state of light and/or entangled photon pairs (or more than 2 photons), for example using Spontaneous Parametric Down-Conversion, wherein this may
  • the quantum light state may interact and/or scatter off of vibrational modes of molecules in the target material, wherein the scattering may include Raman scattering. Additionally or alternatively, during scattering the quantum light state may receive energy from or transfer energy and/or momentum to the molecules it is scattering off of, wherein the type of scattering may correspond to Stokes Scattering and/or Anti-Stokes Scattering. Additionally or alternatively, the methods herein may include detecting or measuring the quantum light state following scattering. Additionally or alternatively, detecting or measuring the quantum light state may be executed using single-photon photodetectors, configured to measure and/or count individual photons and/or quantum photonic states.
  • the methods herein may include determining and/or reconstructing a frequency spectrum, including a Raman frequency shift spectrum, based on measuring the intensity or phase of a quantum light state. Additionally or alternatively, the spectrum determination may include determining the difference in energy between the transmitted quantum state and/or the received quantum state, as caused in whole or in part by the scattering of the quantum state with the material (e.g. stokes or anti-stokes scattering).
  • the method may include generating an entangled photon pairs, passing the pair through an interferometer, such that the entangled photon pairs are transmitted, separated by a beamsplitter or the like, one entangled photon is configured to interact and/or scatters off the target material and/or another entangled photon acts as a reference photon being reflected (e.g. off of a mirror). Additionally or alternatively, the entangled photon pair may recombine and be detected by the photodetector. [00349] Additionally or alternatively, the photon pair may have accumulated a phase shift relative to one another throughout the procedure.
  • the methods herein may include detecting the scattered photons and determining the frequency shift induced by the interaction of the quantum light state with the material and determining a frequency spectrum (e.g. intensity-frequency spectrum), based in whole or in part on the measured frequency shifts. Additionally or alternatively, the method may include measuring a plurality of scattering quantum light states and/or photons (e.g. including amplitudes, and/or phases and/or intensities of the received photons) and determining based on these plurality of measurements a spectrum, including a Raman spectrum.
  • a plurality of scattering quantum light states and/or photons e.g. including amplitudes, and/or phases and/or intensities of the received photons
  • the method includes measuring of the energy shift experienced by the quantum light state, using that to determine a corresponding vibrational energy level of a molecule, and identifying said molecule.
  • the squeezed states may be phase-squeezed or may be amplitude squeezed. Additionally or alternatively squeezing the light states in phase may reduce the noise associated with the quantum phase of the state and/or quantum phase shift of the state, reducing the amount of noise in the interference pattern and enabling a more precise measurement of the phase and/or phase shift, which may be used to determine a more precise spectra, which may be used to determine the faint presence of species in the material (e.g.
  • the detection and/or measurement procedure of the quantum light state may include homodyne detection, which may include configuring a reference beam to amplify the scattered light through resonance for improved measurement. Additionally or alternatively, the detection and/or measurement procedure of the quantum light state may include heterodyne detection, which may include configuring a reference beam with a slightly different frequency to the incoming light, such that the incoming quantum light states and the reference beam may introduce a beating effect for improved measurement.
  • homodyne and/or heterodyne detection methods herein may include determining frequency shifts and/or phase shifts and/or intensity shifts and/or amplitude shifts induced into the quantum state, in whole or in part, based on its interaction and/or scattering with the target material. Additionally or alternatively, in some embodiments, homodyne detection may include determining the interference pattern generated by an interaction between the reference beam and the quantum light state, wherein the use of squeezed states may reduce the noise of the interference patterns making fringes associated with smaller phase shifts more detectable.
  • measuring or detecting the quantum light state may include the use of photon number resolving detectors, which may be used to count the number of individual photons that reach the detector, measure their frequencies and/or frequency spectra, and record their arrival times. Additionally or alternatively, the received quantum light state signal may be amplified through the use of an optical resonance cavity, for more sensitive detection. Additionally or alternatively, the methods herein may include detecting a squeezed light state using an optical resonance cavity, wherein the noise associated with having the photons interact with the resonance cavity may be reduced or attenuated. Additionally or alternatively, the methods herein may also be applicable for quantum spectroscopy based methods wherein the light may not be scattered but absorbed and/or transmitted.
  • the methods herein may be used for molecular-scale imaging to visualize microstructure, nanostructure and/or chemical composition of the material, including that of fresh concrete (e.g. molecules and/or atoms, for each concrete raw material). Additionally or alternatively, the methods herein may further be used to determine calcium silicate hydrate species in fresh concrete (and/or other hydration products). Additionally or alternatively, the calcium silicate hydrate species presence may be used to determine in whole or in part concrete hydration and/or rate of hydration over time. Additionally or alternatively, the methods herein may be used to spatially map the dispersion of chemical admixtures, fibers and the like within the concrete matrix, through quantum- enhanced spectroscopy and/or imaging of the concrete.
  • the methods herein may be used for corrosion determination, for example in whole or in part by identifying chemical species produced during corrosion using super-resolution spectroscopy. Additionally or alternatively, the methods herein may be used to determine delayed ettringite formation (DEF). Additionally or alternatively, the quantum-enhanced spectroscopy methods herein may be used across the concrete value chain, from the raw materials plant and/or quarry all the way to the pour, any may be used to determine and link a particular species and/or portion of raw materials from an earlier stage of the building material lifecycle (e.g. a grain of aggregate in the silo on the batching plant) to a later stage of the building material lifecycle (e.g. identifying that same grain of aggregate in a concrete pour).
  • an earlier stage of the building material lifecycle e.g. a grain of aggregate in the silo on the batching plant
  • a later stage of the building material lifecycle e.g. identifying that same grain of aggregate in a concrete pour.
  • cold atom technologies may be used for sensing, that may employ ultracold atoms for high precision measurements of quantities including gravity, time, acceleration, rotation, and/or magnetic fields.
  • Cold atom technology embodiments include, but are not limited to trapped ions, Bose-Einstein condensates, atom clocks, atom interferometers, and/or optical lattices.
  • superconducting Technologies may employ superconductors (e.g., zero electrical resistance materials that also expulse magnetic fields below a given threshold critical temperature) for high precision measurements. The sensors may be used to measure magnetic fields, electric fields, electromagnetic waves, and/or the like.
  • Sensor embodiments employing superconducting technologies include, but are not limited to SQUIDs, that operate based on the Josephson effect, SNSPDs (superconducting nanowire single-photon detectors), transition edge sensors (TES), superconducting resonators, and/or superconducting tunnel junctions (STJs).
  • SNSPDs superconducting nanowire single-photon detectors
  • TES transition edge sensors
  • STJs superconducting tunnel junctions
  • Other techniques and/or hardware contemplated by embodiments of the present disclosure may include quantum-enhanced spin-echo (e.g., neutron spin echo spectroscopy), quantum state tomography, and/or the like.
  • Quantum Attributes Manipulated or Used for Sensing Embodiments of the present disclosure may exploit a number of quantum attributes for sensing.
  • Quantum States and State Preparation for Metrology may employ any number of quantum state types, state preparation methodologies, and/or a combination thereof. These include, but are not limited to, one or more of: ground and excited states (basic levels of atoms or molecules used in high-precision timekeeping and measurements); coherent states (quantum electromagnetic field conditions that may mimic classical waves; utilized in quantum optics, and imaging); squeezed states (conditions where uncertainty in one variable, such as position or momentum, may be minimized at the cost of increased uncertainty in the complementary variable; used in precise measurement devices); superposition states (where a quantum system exists in multiple conditions at once that may be required for advanced quantum sensors); entangled states (where multiple particles' conditions are interdependent; may be used in advanced communication systems and quantum imaging); fock states (e.g., number states) (defined particle numbers, such as photons or electrons that may be employed in quantum optics and specific interferometers); bell states (highly interlinked pairs of qubits; may
  • Quantum Timekeeping may employ quantum timekeeping to enable high accuracy and precision time measurement.
  • Quantum timekeeping devices may include atomic clocks (e.g., cesium atomic clocks, rubidium atomic clocks, hydrogen masers, and/or the like), optical clocks, ion clocks, quantum logic clock, and/or the like.
  • quantum clocks may be used for more accurate time of flight evaluations and analyses and other signal processing contemplated herein. Further, time of flight analysis may be used for positioning, deflection analysis, and/or the like.
  • Quantum magnetometry may provide a high accuracy measurement of magnetic fields, particularly in spatial domains. In some embodiments, this may be used for spatial mapping of time varying electric and/or magnetic fields across materials. Sensor technologies may include SQUIDS or Room Temperature SQUIDs.
  • a magnetometry method may include actuating a material using an electromagnetic wave (e.g., an RF wave). Additionally, or alternatively, the method may include receiving the wave post-interaction with a target material (e.g., as a reflection, transmission, scattering, or any other interaction type).
  • an electromagnetic wave e.g., an RF wave
  • the method may include receiving the wave post-interaction with a target material (e.g., as a reflection, transmission, scattering, or any other interaction type).
  • the method may include the electromagnetic wave being generated using a classical antenna. Additionally, or alternatively the method may include the interacting electromagnetic wave being received back after the interaction by a quantum magnetometer system.
  • a determination about material characteristic based on measured magnetic component may be made. Additionally, or alternatively, a determination about material characteristic may be made based on a difference between magnetic component between interacting with material and after interacting with material. Additionally, or alternatively, a determination about a material characteristic may be made based on temporal evolution of magnetic field component of the electromagnetic wave.
  • a multi antenna system may be used to sweep electromagnetic waves at a very large range of frequencies.
  • the magnetic component of the wave may be measured at all these frequencies (e.g., wave impedance spectroscopy).
  • wave impedance spectroscopy e.g., wave impedance spectroscopy
  • classical systems require large antennas and small antennas to measure fields at very low frequencies and high frequencies respectively. In other words, it is almost impossible to create a miniaturized device capable of performing all of said functions.
  • Embodiments, of the present disclosure facilitate this such that a design of a single device may sense all these kinds of waves and their interactions with materials.
  • Quantum Electrometry [00364]
  • Embodiments of the present disclosure may include quantum electrometry sensing techniques.
  • the wave may be electromagnetic radiation wave. Additionally, or alternatively, the wave may be a matter wave (e.g., wave-particle duality - use particles/atoms as waves). Additionally, or alternatively, the wave may be a gravitational wave. Quantum Gravimetry and Gradiometry [00367] Embodiments of the present disclosure may contemplate the use of quantum gravimetry and gradiometers to provide measures of absolute and relative gravitational field strength (respectively). Quantum gravimetry and gradiometers may be employed extensively for density or mass related determinations.
  • the simulation may include a simulation of the gravitational field in the absence of the target material. Additionally, or alternatively, the simulation may include a simulation of the gravitational field were the material not present. Additionally, or alternatively, the method may include determining the gravitational field contribution from the material. Additionally, or alternatively, the method may include determining the gravitational field contribution from the material based in whole or in part on the measured gravitational field, and/or the simulated gravitational field and/or the difference between the two. Additionally, or alternatively, a density or density distribution, specific gravity or the like, of the material may be determined based on the gravitational field sensor measurement.
  • the acceleration measurement may include measuring gravitational acceleration evolution over time.
  • Sensor Device & Sensor Method Functioning the quantum systems herein for gravitational field measurements may include atomic interferometers. Additionally, or alternatively, this may include cold-atom or Rydberg atom interferometry-based systems. Additionally, or alternatively, the cold-atom interferometry-based system may include the use of matter waves. Additionally, or alternatively, wherein the matter waves may include caesium-atom and/or rubidium atom-based matter waves.
  • the quantum systems may include molecule-based matter wave interferometry-based systems, wherein the species whose quantum states are being manipulated to make the measurement may include a collection of atoms / a molecule.
  • the atomic interferometry systems and/or methods herein may include cold-atom quantum state preparation, configuring the quantum state(s) to free fall in vacuum or near vacuum, splitting the quantum wavefunction(s) into a superposed state(s), wherein each superposed state(s) may be configured to take separate paths under the influence of gravity, recombining the quantum wavefunctions into the same path(s), measuring the quantum state(s).
  • the cold-atom interferometry method and/or system may include the steps, carried out for a cloud of atoms (a cloud may include a plurality of atoms of the same species e.g. caesium), which may include measuring the resultant quantum state of all or a plurality of atoms in the cloud having passed through the interferometry procedure.
  • quantum state preliminary preparation may include preparing a sample (e.g., cloud) of atoms of a desired species (e.g. caesium), and laser cooling the sample for example in a magneto-optical trap to near-absolute zero temperatures (e.g. microkelvins or below).
  • every atom in the atom cloud may be an individual quantum state.
  • the system may include a vacuum chamber wherein the atom cloud is configured to free fall after initial preparation. Additionally, or alternatively, every atom in the atom cloud may behave as a matter wave.
  • the method and/or system may include altering the quantum states of the atoms in the cloud during free fall by using lasers. Additionally, or alternatively, the method and/or system herein may alter the quantum state by using one of a Raman and/or a Bragg laser pulse, which may be used a plurality of times. Additionally, or alternatively, altering the quantum state may include inducing a superposition in the quantum state.
  • altering the quantum state may include transferring momentum from the laser to the atom cloud species, to generate a superposed quantum state wherein one basis state may include a ground state of the atom and another basis state may include an excited state, wherein the difference in momentum eigenvalues between the two states may be ⁇ p, wherein p represents the momentum carried by the laser photons.
  • the ground and excited states may be different states based on the hyperfine electronic structure of the atomic species.
  • the method and/or system may include a plurality of laser pulses, configured to alter the quantum states and/or the spatial paths of the atom cloud in free fall.
  • the method and/or system may include a first laser pulse configured to induce a superposition state in the atoms in the atom cloud, which may induce a change in the path travelled between the two quantum basis states, a second laser pulse configured to swap the two states, which may induce the two quantum states to spatially rejoin, and a third laser pulse configured to be used at the moment where the two quantum states spatially rejoin, configured to alter the superposition state such that the two matter wave states re-combine.
  • the method and/or system includes the quantum states accumulating a phase shift throughout free fall, which may be induced by the one or more laser pulses shone onto the atom cloud.
  • the induced phase shift may in whole or in part be induced by gravitational fields.
  • the method herein may include measuring the quantum states after being recombined, to determine the phase shift, and further using the phase shift to determine the gravitational field strength and/or acceleration at the sensor location. Additionally, or alternatively, the method may include measuring an interference pattern generated by quantum states in the atom cloud (which may be treated herein as matter waves), determining one or more phase shifts based on the interference pattern, and determining a measure of the gravitational field based in whole or in part on the phase shift or the interference pattern.
  • the method herein may include measuring a plurality of quantum states from the plurality of atoms in the atom cloud, determining based on the plurality of measurements, one or more probability or probability distributions for the atoms to be in each of its superposed states, determining the phase difference based on the one or more probability or probability distributions, and/or determining a measure of the gravitational field strength based in whole or in part on the phase change.
  • measuring the quantum state of an atom and/or matter wave may include determining the quantum basis state the wavefunction collapses to.
  • determining the basis state may include shining a laser onto the atom, wherein the laser may be configured to transmit energy quanta to create an electron transition in one of the two quantum basis states (but not the other). Additionally, or alternatively, determining the basis state may further include using a photodetector to measure light emitted from the atom, wherein this may include measuring the fluorescence emission from the electron subsequently falling back to its previous state (after having been excited by the laser). Additionally, or alternatively, the photodetector may determine the frequency of the emitted light. [00373] Additionally, or alternatively, the method may include determining which basis state the atom is in based in whole or in part on the frequency of the photodetected light.
  • the laser may excite a plurality of atoms in the atom cloud
  • the photodetector may be configured to count the number of photons of a particular frequency that have been emitted, wherein the frequency may be correlated with the energy state of the atomic species basis state, and wherein the count may be representative of the number of atoms present in the basis state, inside of the atomic cloud.
  • the probability and/or population of atoms in this basis state may be determined based on the count and/or the total number of atomic species (which may be predetermined or known during state preparation).
  • the method and/or systems herein may include a time synchronization system and/or step, which may be used to activate lasers at the right time during free fall of the atomic cloud.
  • the time synchronization system may be a quantum clock.
  • the lasers included in the system and/or as part of the method may include cooling lasers, Raman lasers, optical pumping lasers, and/or detection lasers.
  • the methods and/or systems herein may include diode lasers, fiber lasers, Ti:Sapphire lasers, and/or the like.
  • the cooling lasers may include lasers generating light at 780nm for cooling of rubidium and/or rubidium clouds, and/or lasers generating light at 852nm for cooling caesium and/or caesium clouds.
  • the quantum states herein may be entangled quantum states for increased sensitivity. Additionally, or alternatively the entangled states may include a plurality of atomic species in the atomic cloud entangled together.
  • the entangled quantum states may be NOON and/or GHZ states.
  • state preparation may include preparation of one or many NOON and/or GHZ states including a plurality of atoms entangled together.
  • the procedure described herein for atomic interferometry may be carried out for the NOON and/or GHZ state.
  • this may include configuring the NOON and/or GHZ state to free fall, shining lasers onto the NOON and/or GHZ state atoms to alter the NOON and/or GHZ state such that the superposed NOON and/or GHZ state accumulates a phase shift, wherein the phase shift may grow proportionally to the number N of entangled atoms in the NOON and/or GHZ state, shining lasers onto the NOON and/or GHZ state atoms to recombine the superposed state, measuring one or a plurality of NOON and/or GHZ states and/or their interference patterns, determining a phase shift based in whole or in part on the measurement, and determining a measure of the gravitational field in whole or in part based on the phase shift.
  • the gradient may be associated with a change of field strength with respect to space and/or the derivative of the change in field strength with respect to space and/or position.
  • the plurality of quantum sensing devices’ spatial positions may be changed to determine the field gradient over space.
  • the method may include measuring the field strength using two or more quantum sensing devices (e.g. gravimeters, electrometers, magnetometers), determining the difference between the field values, and/or determining the field gradient between the two spatial locations of the sensors, in whole or in part based on the measured field strength difference and/or the spatial distance between the two measurement locations.
  • the plurality of quantum sensing devices may form an array.
  • control system and time synchronization module may be used to activate lasers to target different atom clouds, simultaneously.
  • gradiometry methods herein may include entangling two or more quantum states, moving these quantum states such that they are spatially separating, and determining a field gradient based in whole or in part on these entangled states and/or a measurement made onto these entangled states.
  • this may include generating an entangled states made of one or more quantum states, using a quantum communication channel to transfer each state to a plurality of quantum sensing devices, each quantum sensing devices receiving the entangled state, and/or measuring a field strength and/or field gradient based on, in whole or in part, measurements made by one or a plurality of quantum sensing systems based on the entangled states delivered to them.
  • the methods herein may include simulating an expected local gravitational field. Additionally, or alternatively, the simulated gravitational field may be simulated in whole or in part based on the presence and/or location of a volume of the target material. Additionally, or alternatively, the methods may further include measuring a gravitational field using a gravimeter and/or quantum gravimeter or measuring changes/gradients in gravitational fields based on gravimeters.
  • the gravimeter may be positioned in proximity with the target material, such that the measured gravitational field is influenced in whole or in part by the presence (or the mass) of the target material, wherein measuring the gravitational field disturbance may only be possible with quantum gravimeters due to increased sensitivity.
  • the quantum gravimeter may include a cold-atom interferometry-based system.
  • the methods may further include determining a difference between the expected and/or simulated gravitational field, and the measured gravitational field. Additionally, or alternatively, the methods may further include use of a model such as a prediction model to determine a characteristic of the target material.
  • the method may include determining one or a plurality of gravitational field measurements in proximity to a target material, wherein the plurality of measurements may be taken over different points in space and/or over time (continuously or discretely), reconstructing the local gravitational field based in whole or in part on the gravitational measurements, determining the bulk density and/or density distribution of the material volume in whole or in part based on the gravitational field measurement(s) and/or the reconstructed local gravitational field. Additionally, or alternatively, the method may include determining the density and/or density distribution based on the gravitational field disturbances caused by the volume of target material as well as its microstructure.
  • the system and/or methods herein may include actuating the target material. Additionally, or alternatively, actuation may include generating a wave and/or a field and/or an oscillation and/or any other disturbance, and making it interact with the target material. Additionally, or alternatively, actuation may include generating the disturbance from an antenna, having the antenna generate a wave directed at the material, and having the wave actuate, excite and/or otherwise interact with the target material. Additionally, or alternatively, the disturbance may include a mechanical wave, electromagnetic wave, gravitational wave, electromagnetic field, a radio frequency wave, a microwave, light, and the like.
  • the method and/or system may be configured to sense a fluctuation in the gravitational field induced by the interaction between the actuation/disturbance, and the target material. Additionally, or alternatively, the method and/or system herein may be used to determine a property of the target material based in whole or in part on the gravitational field fluctuation and/or plurality of fluctuations.
  • the term “actuating” may be taken to mean the operation of an “actuator” as defined elsewhere herein e.g. generating a wave and/or signal that is set to interact and/or create a disturbance with a target material.
  • a mechanical wave determining another or another set of gravitational field measurement(s) and/or disturbances whilst or after the material is actuated, using these measurements to determine another one or more density or density distribution measurements which may be associated with the actuation wave causing displacements of mass in the material, and/or determining, based in whole or in part on one or more of a difference between the determined density or distributions pre-actuation and post-actuation, a difference between measured gravitational fields pre-distribution and post-actuation, a characteristic of the actuation wave (e.g. amplitude, shape such as sinusoidal or square, wavelength/frequency, phase, and the like%), a mechanical property of the material.
  • a characteristic of the actuation wave e.g. amplitude, shape such as sinusoidal or square, wavelength/frequency, phase, and the like
  • the methods herein may further include determining, based on a characteristic of the wave (e.g. amplitude, shape such as sinusoidal or square, wavelength/frequency, phase, and the like%), as well as the material response to the wave as measured by the shifting gravitational fields and/or density distributions, a mechanical property of the material.
  • a characteristic of the wave e.g. amplitude, shape such as sinusoidal or square, wavelength/frequency, phase, and the like
  • the mechanical property may be determined using measured time varying gravitational fields (e.g. strength, gradient and the like), and/or time varying density and/or density distributions (which may in whole or in part be caused by the wave and/or disturbance generated by the wave produced by the actuator.
  • the methods herein may be used to determine the presence, distribution, size, shape, and/or proportions of a subspecies of material that makes up the composite material, within a volume or subvolume of the composite material, based in whole or in part on the density distribution data generated by the quantum gravimetry methods herein.
  • the method may further include using in whole or in part an expected density of the material subspecies within the target material.
  • the method may further include generating a simulation of the expected local gravitational field based on the presence of target material subspecies in the target material and comparing the simulation with the measurement results from the quantum gravimetry systems herein.
  • the composite material may include a cementitious mix such as concrete.
  • the methods herein may include spatially mapping a volume of the cementitious mix based in whole or in part on gravitational measurements from within and/or on the surface of and/or in proximity to the cementitious mix. Additionally, or alternatively, the spatial mapping may be done in whole or in part based off of the difference in densities between the different type of concrete raw materials and/or compositional elements, including aggregates, cement and/or cement matrix, pores and/or water which may each have different densities.
  • the methods herein may be used to determine and/or identify use of a plurality of binders and/or binder types within a cementitious mix, including but not limited to the presence of Portland cement, fly ash, slag and the like. Additionally, or alternatively, the methods herein may be used to monitor the temporal evolution of concrete. [00386] Additionally, or alternatively, this may include the temporal evolution of concrete as it is curing and/or transitioning from fresh concrete to hardened concrete. Additionally, or alternatively the methods and/or gravimetry sensing methods herein may be used to determine data indicative of the rate of hydration of concrete and/or concrete strength as it cures over time.
  • these concrete time evolving determinations may in whole or in part be made based on the shifting densities and/or density distributions of concrete mixes as they transition from their fresh to their hardened state. Additionally, or alternatively, the methods herein may be used to determine the evolution of water content in the concrete as it cures based in whole or in part on the density of water, and the difference between the density of water and that of other raw materials in concrete. Additionally, or alternatively, the rate of hydration of the concrete may be determined in whole or in part based on the water content determinations. Additionally, or alternatively, mechanical properties of concrete such as concrete compressive strength, modulus of elasticity, stiffness and the like may be determined in whole or in part based on the quantum gravimetry methods listed herein.
  • this may include actuating the concrete using a mechanical wave and determining the gravitational field strength and/or gradient fluctuations which may in whole or in part be generated by the mass redistributions caused by the actuating wave. Additionally, or alternatively, the one or more mechanical properties may be determined in whole or in part based on the gravitational field fluctuations. Additionally, or alternatively, any of the methods listed herein may be used for void mapping. Quantum Sensing For Concrete [00387] In some embodiments, the quantum measurement methods and/or systems herein may be used for determining attributes of composite materials (e.g., the target material is a composite material). Additionally or alternatively, the composite material may be a cementitious mix. Additionally or alternatively, the cementitious mix may include concrete.
  • composite materials e.g., the target material is a composite material. Additionally or alternatively, the composite material may be a cementitious mix. Additionally or alternatively, the cementitious mix may include concrete.
  • the methods and/or systems herein may be used to spatially map the microstructure of concrete. Additionally or alternatively, the methods and/or systems herein may be used to determine data associated with voids in concrete (e.g. the presence and/or location and/or shape and/or size and/or type of voids in concrete, which may include for voids at the millimeter scale and less). Additionally or alternatively this may include spatially mapping and/or imaging of air voids in a volume of concrete material.
  • the methods and/or systems herein may include using in whole or in part, data generated from a quantum electrometry and/or a quantum magnetometry based systems, such as the ones disclosed herein, to determine the void data. Additionally or alternatively, the methods herein may include determining a measure of an electromagnetic field strength and/or gradient using any of the magnetometry and/or electrometry based quantum systems herein (e.g. NV-Center diamond based systems, silicon carbide based systems, quantum dots and the like). Additionally or alternatively, this may include determining a plurality of electromagnetic field strength measurements, including measurements at separate locations in space and/or time-series measurements to characterize temporal evolution of fields.
  • a measure of an electromagnetic field strength and/or gradient using any of the magnetometry and/or electrometry based quantum systems herein (e.g. NV-Center diamond based systems, silicon carbide based systems, quantum dots and the like). Additionally or alternatively, this may include determining a plurality of electromagnetic field strength measurements, including measurements at separate locations in space and
  • the methods and/or systems herein may include determining one or more electromagnetic field strength and/or gradient measurements based in whole or in part on measurement of quantum state and/or a change in quantum state energy levels, using the one or more electromagnetic field strength and/or gradient measurements to determine data indicative of any electromagnetic attribute or property of the concrete including its dielectric constants, electric permittivity, magnetic permeability, and the like and/or determining the data associated with air voids in concrete (e.g. presence of air voids, location of air voids and the like), based in whole or in part on the determination of the one or more electromagnetic field measurements, wherein the presence and/or type and/or density distribution and/or shape of air voids induce minute differences in the electromagnetic properties of the material.
  • the methods herein may be used to determine different types and/or species of void and discern between them. Additionally or alternatively, this may be done in whole or in part based on a difference in electromagnetic and/or gravitational field fluctuations caused in whole or in part by the difference in void species and/or type.
  • the methods herein may further include using an actuator to drive a signal and/or excitation and/or wave and/or field into the target material. Additionally or alternatively, this actuator may be configured to generate mechanical, electromagnetic and/or gravitational signals and/or waves and/or fields. Additionally or alternatively, the actuator may apply an electric current, magnetic field, electromagnetic wave and/or mechanical wave onto the material.
  • the method may include determining concrete void data associated based in whole or in part on the electromagnetic and/or gravitational field fluctuations caused by the material interacting with the actuator wave. Additionally or alternatively, the wave interaction with the material may vary based in whole or in part on the concrete microstructure. [00391] Additionally or alternatively, different raw materials and/or microstructure attributes of concrete may induce different interactions with the actuator wave (e.g. change wave speed, increase or decrease wave attenuation, increase or decrease wave impedance). Additionally or alternatively, these variations may generate different disturbance signals in the measured field strengths and/or gradients. Additionally or alternatively, the presence of different concrete raw material species (e.g.
  • aggregates, cement/binders, cement/binder matrix, water content, voids, air voids, carbon dioxide voids and the like), and/or variations in the concrete microstructure may be determined in whole or in part based on the difference between variations caused by different species or subspecies or microstructure volumes with different attributes. Additionally or alternatively, any of the quantum methods described herein may be used to determine and/or spatially map aggregate content, a cement matrix, water content, air voids, pores and/or other compositional or structural features of the concrete.
  • the methods herein may include using an actuator to generate an electric field and/or current in the concrete and/or induce ion flow at the atomic or molecular level within the concrete microstructure, determining one or more electric or magnetic field measurements over time using any of the quantum sensing systems and/or methods described herein, determining a rate of ion flow through the concrete based in whole or in part on the electric or magnetic field measurements.
  • the methods herein may include using arrays of quantum sensing systems, embedded into the material or placed in proximity or on the surface of the material to measure electric fields and/or magnetic fields and determining material attributes such as mapping microstructure and/or determining ion flow at the atomic or molecular level within the microstructure.
  • the methods and/or systems herein may include the use of quantum magnetometry and/or electrometry for rebar mapping.
  • this may include in whole or in part, determining one or more electric and/or magnetic field measurements associated with one or more concrete volumes which may include rebar (e.g. reinforcement steel). Additionally or alternatively, this may include determining the presence and/or location of the rebar based in whole or in part on the electric field and/or magnetic field measurements (e.g. field strength and/or field gradients). Additionally or alternatively, this may further include determining a magnetic and/or electric field disturbance correlated to presence of rebar inside of concrete. Additionally or alternatively, the disturbance in determined based on the difference between the expected and/or simulated field with no presence of rebar and/or the measured field.
  • rebar e.g. reinforcement steel
  • this may further include determining a magnetic and/or electric field disturbance correlated to presence of rebar inside of concrete. Additionally or alternatively, the disturbance in determined based on the difference between the expected and/or simulated field with no presence of rebar and/or the measured field.
  • the presence and/or location of rebar may in whole or in part be determined based on magnetic or electric field fluctuations due to the metallic and/or electromagnetic properties of the rebar and/or based on the difference between the simulated field without rebar and the measured field.
  • the methods herein may further include using an actuator to generate electric or magnetic fields and/or waves and/or excitations in the concrete, measuring one or a plurality of electric and/or magnetic fields strengths and/or gradients and/or disturbances, optionally over time, determining the presence or absence and/or location of rebar inside of the concrete based in whole or in part on the measured field values and/or fluctuations which may have been induced by the interaction of the actuator signal and/or the concrete and/or the rebar. Additionally or alternatively, the methods herein may further include the use of quantum-enhanced ground penetrating radar based methods to determine the location of rebar, as well as the presence of defects in the concrete and/or other elements embedded within the concrete (e.g.
  • the methods herein may include determining, using a quantum gravimeter and/or quantum gravity gradiometry system, context awareness data associated with the concrete, as defined elsewhere herein. For example, this may include using quantum gravimetry based methods to determine the geometry of a pour. Additionally or alternatively, in some embodiments, the methods and/or systems herein may include embedded one or a plurality of quantum gravimeters inside of a concrete sample, and determining, in whole or in part based on gravitational field measurements (e.g.
  • the boundaries of the pour in space may include using the quantum gravimetry system(s) to measure gravitational field data and based in whole or in part on the gravitational field data determining a difference in density or density distribution between the volume defining the concrete and the density or density distribution of the external environment (e.g. air). Additionally or alternatively, the one or more gravitational measurements may be taken at separate locations within the concrete pour. Additionally or alternatively, the one or more gravitational measurements may be used to reconstruct the geometry of the pour based on the density difference present at the boundaries of the pour, between the concrete pour itself and its environment.
  • the measured electromagnetic field may be in whole or in part be used to determine the water content and/or water-to-cement ratio in the concrete, based in whole or in part on the electromagnetic properties of water e.g. dielectric constant, conductivity, resistivity and the like.
  • the measured gravitational field may in whole or in part be used to determine the water content and/or water-to-cement ratio and/or hydration rate of reaction in concrete, based in whole or in part on fluctuations in density distribution.
  • the methods herein may include measuring a plurality of gravitational or electromagnetic (e.g.
  • the methods may further include determining one or more changes in water content and/or one or more rate of hydration over time as the concrete hardens, in whole or in part, based on measured fluctuations in field strength and/or field gradients over time. Additionally or alternatively, the determination of water content and/or rate of hydration may be done in whole or in part based on the change in magnitude of variations over time as the concrete hardens. Additionally or alternatively, this may include a decrease in gravitational field fluctuations as the concrete structure stiffens and less mass redistribution is happening.
  • the methods herein may include making multiple determinations of water content over time and based on these measurements determining in whole or in part a concrete hydration rate.
  • Quantum Gravimetry For Material Deformation may include the use of quantum gravimeters, quantum inertial sensors, and/or quantum accelerometers and the like for structural health monitoring. Additionally or alternatively, this may include the use of cold-atom interferometry based devices for gravitational field strength and/or gradient measurement. Additionally or alternatively, this may include embedding quantum sensing devices inside of concrete (or other material) in the structure to monitor the structural shifts of the structure.
  • the deformations may include concrete shrinkage, structural tilting, material expansion (e.g. concrete expansion), structural settling, creep and/or other such deformations, including concrete related deformations.
  • the methods may further include determination of voids and/or cracks in or under the concrete and/or structure based on the gravitational field measurements, gravitational field changes measurements and the like. Additionally or alternatively, the determined deformations may include millimeter-scale deformations. Additionally or alternatively, presence and location of cracks and/or voids may be determined in whole or in part based on the density difference between the crack and/or void, and the structure material (e.g. concrete).
  • the methods herein may further include determination, localization, and/or spatial mapping of mechanical forces and/or attributes which may cause deformations, in whole or in part based on the gravitational field measurement(s) and/or the deformation determinations. Additionally or alternatively, such mechanical forces and/or attributes may include shears, stresses, strain and the like. Additionally or alternatively, the methods herein may include mapping boundaries of concrete elements, and determining shrinkage based on the evolution of boundary locations which may be derived in whole or in part on gravitational field measurements.
  • the methods herein may include using the quantum magnetometry and/or electrometry systems described herein to determine in whole or in part the presence and/or location of a crack and/or void in concrete. Additionally or alternatively, the methods herein may include using these systems to determine an electric and/or magnetic field measurement and determining corrosion inside the concrete based in whole or in part on the electric and/or magnetic field measurements. Additionally or alternatively, the corrosion may be a corrosion of concrete and/or may be a corrosion of rebar embedded in concrete.
  • the methods herein may include determination of the corrosion of rebar based in whole or in part on measuring a minute change in magnetic and/or electric field, corresponding to an expected change in electric and/or magnetic field induced by a corrosion reaction. Additionally or alternatively, the methods herein may further include determining the location of the corrosion based in whole or in part on the one or more electric field and/or magnetic field measurements.
  • the methods herein may further include using an actuator to generate an electric or magnetic field, electric current, and/or electromagnetic wave in the concrete material, wherein the generated actuator disturbance may interact with the concrete and/or rebar in the concrete differently based on the presence or absence of corrosion, and determining, based on the measured electric and/or magnetic fluctuations cause by the actuator disturbance, one or more presence and/or location of corrosion. Additionally or alternatively, the magnitude of the field strength change and/or disturbance may be used to determine the type of corrosion (e.g. concrete corrosion may induce weaker field disturbances than rebar corrosion).
  • the methods herein may include measuring one or more electric and/or magnetic field strengths and/or gradients, determining based on the measured field(s), the presence of electrochemical reactions correlated to the corrosion chemical process, as well as magnetic fields generated by the electrochemical reactions, determining based on the measured fields and/or the presence of electrochemical reactions, the presence and/or location of corrosion in concrete and/or in the concrete rebar. Additionally or alternatively, the methods herein may further include spatial mapping of corrosion throughout the concrete volume, using any of the methods described herein. Additionally or alternatively, the mapping of corrosion may be determined in whole or in part based on the mapping of electrical and/or magnetic anomalies in the concrete.
  • a plurality of classical post- processing techniques and/or experimental design techniques may be applied to the system herein to reduce the size of mathematical error bars in measurement outputs including but not limited to the use of stochastic and/or optimization techniques such as maximum likelihood estimation.
  • the system may include Dynamic Decoupling (DD) techniques to moderate and apply radiation of a variety of electromagnetic sources including but not limited to optical lasers, ultraviolet, and microwave pulses wherein pulses may be sent in patterns to mitigate environmental effects and reduce signal noise including but not limited to techniques such as sending pulses to stimulate changes in the system (e.g. spin flips in materials) on a timescale faster than the corresponding environmental noise response preserving the quantum state.
  • DD Dynamic Decoupling
  • radiation of a plurality of types including but not limited to electromagnetic radiation may be dynamically polarized where in devices and/or models may dynamically adjust the polarization of applied radiation such that the environmental effects on the system being measured are reduced.
  • polarization filters may be used to allow only specifically polarized radiation to interact with the system including but not limited to filtering and/or polarizing light in linear polarizations and/or circular polarizations.
  • systems and methods may include the use of wave plates and/or a plurality of optical devices including but not limited to full-wave waveplates, half- wave waveplates, quarter-wave waveplates, birefringent crystal materials, and/or polarization beam splitters to affect radiation in the system.
  • the system may include dynamic adjustment of the devices and techniques applied to measurements to continuously and/or asynchronously adjust the state of the system in order to reduce system noise and/or environmental affects (e.g. system relaxation, backscattering, temperature changes, polarization changes, spatial positional changes).
  • devices and systems may include a plurality of lock-in amplifier techniques and/or devices to help isolate, amplify, and/or adjust different observed signals for purposes of improving the system performance including but not limited to reducing the mathematical error in measurements and characteristics determined by the system and/or measuring phase shifts in signals of a plurality of types. Additionally or alternatively, these techniques may be repeatedly applied and/or dynamically adjusted and/or used to determine system changes over time. Additionally or alternatively, in some embodiments systems may include a plurality of phase sensitive detection devices. Additionally, or alternatively, in some embodiments the data output and/or external inputs may be analyzed using a plurality of techniques including but not limited to stochastic, numerical, and statistical methods to analyze data sets and reduce error across systems.
  • the methods and/or systems herein may include methods or systems for spatially and magnetically mapping, sensing, detecting, and/or locating the entirety or various portions or parts of built or natural structures including but not limited to the large metallic elements (e.g., rebar, submerged equipment or aquatic structures, below ground infrastructure, metallic features in natural rock formations). Additionally, or alternatively, this may include using quantum effect-based sensing for determination of magnetic fields and characteristics of the structures and materials.
  • large metallic elements e.g., rebar, submerged equipment or aquatic structures, below ground infrastructure, metallic features in natural rock formations.
  • different types of radiation may be applied multiple times or in different configurations including applying green light or ultraviolet light before and after the application of microwaves and some embodiments may include rest periods or other activities between applications. Additionally, or alternatively, some embodiments may include applications of laser radiation and/or microwaves including but not limited to continuous-wave microwave excitation, pulsed microwave techniques (e.g. pulse adjustment to enact dynamic decoupling), and/or polarization modulation of laser light (including high frequency switching between various polarizations) to help increase performance of the system including increasing precision of the information generated.
  • pulsed microwave techniques e.g. pulse adjustment to enact dynamic decoupling
  • polarization modulation of laser light including high frequency switching between various polarizations
  • systems herein may use quantum optics techniques including adjusting the properties of light fields such that the uncertainties of certain properties are reduced below the standard quantum limits allowing for high precision measurement of certain material and/or environmental characteristics including but not limited to the strain, temperature, phase, temperature, and/or magnetic field.
  • the system may include devices that stimulate a laser field and/or other photon fields in a plurality of ways and then apply them to a variety of materials including but not limited to nonlinear mediums (e.g.
  • nonlinear crystals or optical fibers to facilitate processes for squeezing light via techniques including but not limited to parametric down-conversion and/or four-wave mixing which produce quantum correlated photons in different modes that exhibit the desired quantum optical uncertainty behavior allowing for high precision measurement.
  • electric fields may be applied to the light to induce squeezing effects.
  • photodiode or homodyne detectors are then used in the system to measure characteristics of the squeezed light including intensity when combined with a locally generated reference beam, amplitude, and/or phase which can then be analyzed to determine the quantum properties of the light.
  • the system may feed data into models for methods of material determination and analysis including but not limited to machine learning and/or quantitative analysis which may be used to determine characteristics or target properties of materials and structures including but not limited to spatial distribution, magnetic field, electrical charge, density, and/or constituent chemical composition. Additionally, or alternatively the system may include methods to analyze the changing of observed or determined properties over spatial or time domains. Additionally, or alternatively, the system may include methods to dynamically adjust to information (e.g., change device or material positions in space, change frequencies of electromagnetic radiation, etc.).
  • information e.g., change device or material positions in space, change frequencies of electromagnetic radiation, etc.
  • user input or data output from a plurality of models including those unrelated to the system may be used to adjust the systems behavior (e.g., change device or material positions in space, change device activation patterns, change materials, and/or change models being applied).
  • arrays of sensors and associated devices may be placed in specific arrangements including in rings or lattices surrounding structures or alongside them in a plurality of layouts including arrays of varying curvature in 3-dimensional space.
  • quantum sensors contemplated by the present disclosure include quantum acoustic and mechanical sensing, quantum imaging, quantum RADAR and LIDAR, quantum communications and microwave sensing (e.g., increase sensitivity sensing for longer range communications), quantum terahertz sensing, quantum microscopy, quantum thermometry, quantum spectroscopy (in particular any of the spectral techniques described herein augmented with quantum states for higher accuracy measurements), and/or quantum and NMR hyper polarization in nuclear spins.
  • Quantum Device Applications Material Property Determination may be used to measure time evolving properties of materials (e.g., curing rate of concrete).
  • said sensors may be used for all of the same applications wave-based sensors may be used for.
  • static material properties, contextual material properties, and/or the like may be determined using quantum sensors.
  • Material Composition Determination and Identification [00408] In some embodiments, material composition and formulations may be determined using quantum sensors. In particular, high resolution, atomic level, and/or molecular level maps may be generated, that may then be stored in quantum memory and may be measured using quantum sensors. These may be input into machine learning models for material design, optimization, generation, and/or the like described herein.
  • Quantum Devices and Communications Hardware Architecture contemplate a plurality of hardware architectures for implementation of such quantum sensors into devices (e.g., as described herein as ‘smart devices’).
  • a non-exhaustive list may include quantum and classical wireless sensor networks, batteries, quantum data storage, classical data storage, quantum state preparation circuitry, optical components, frames and resonance modes, cavities, cooling and regulation systems, and/or the like.
  • Quantum Error Correction [00410] Quantum error correction methods may be employed by quantum computers, quantum sensors, and generally any quantum device contemplated herein to ensure stability in different operating environments (e.g., when in proximity of target materials).
  • Quantum Sensors Distributed networks of Quantum Sensors that may communicate over a classical or quantum channel, to each other, or to quantum or classical computers are also contemplated by embodiments of the present disclosure.
  • Quantum Communications Devices may inherit any of the features of the hardware sections herein. Additionally, or alternatively, the devices may communicate over quantum channels, that may include photonic quantum channels, or the exchange of entangled particles, and/or the like.
  • Hybrid Communications [00413] Hybrid devices (e.g., devices employing partly quantum and partly classical features) may convert quantum data into classical information (e.g., through the collapse of the wave function, through a classical representation of a quantum state, and/or the like).
  • the hybrid devices may be passed on to classical chipsets (e.g., CPUs or GPUs) for further transmission through classical channels.
  • quantum information may also be sent to a quantum analogue of any hubs or gateways described herein.
  • Quantum Sensors for Communication [00414]
  • high accuracy quantum sensors may be employed for long range communication and for communications in noisy environments. Super resolution techniques may be employed for such embodiments.
  • sensors may be embedded deep into a material and wireless signals may be received outside of the material using quantum communication receivers.
  • Other Embodiments [00415] Additional embodiments contemplated by the present disclosure may be include quantum impedance spectroscopy as a method for material determination.
  • active quantum sensors may take the form of quantum gates which may act on a target material or on target matter and interactions which are then measured through another quantum gate in contact with the material.
  • a prepared quantum state may be allowed or may be made to interact with a material or matter, and then measured.
  • the embodiments or techniques herein may involve interaction of or with any of one or more of the following particle and field types: weak force interactions and its gauge bosons (W+/W- and Z bosons); strong force interactions (quarks) and its gauge bosons (gluons); electromagnetic field and force interaction and its gauge bosons (photons); gravitational force interaction and its gauge bosons (graviton); Higgs field interaction; fermionic interaction (both as waves or particles, including electrons, muons, tau particles, neutrinos, and other quasiparticles).
  • any technique herein may be embodied onto a low power or ultra-low power smart sensing device that may inherit any of the properties of devices described elsewhere, including, for example, smart embeddable sensors.
  • quantum informational theories may be used to refine measurements from quantum sensors or devices.
  • quantum dynamics may be used to connect networks of distributed quantum sensors together. In some embodiments, entirely new sensors exploiting any of the quantum methods described herein may be produced.
  • new probe states that may be maximally sensitive to a given system may be produced.
  • room temperature SQUIDs may be employed.
  • nanowire/quantum wire array-based sensors with quantum efficiency may be used.
  • nanowire array-based cameras may be used.
  • JTWPAs Josephson Travelling Wave Parametric Amplifiers
  • JPCs Josephson Parametric Converters
  • quantum phase slip junctions may be used.
  • single photon quantum LIDAR may be employed to detect small defects of materials or matter (e.g., anisotropies in amorphous materials).
  • any of the quantum sensors or quantum devices herein may employ frames or resonators or their quantum analogue as described herein.
  • quantum frames or quantum resonators may influence quantum properties of a system (e.g., quantum tunnelling).
  • quantum sensors or devices or metrology may be used to map out a material or matter-based system (e.g., at the atomic or molecular level or, if the context requires it, at the particle level). This map may be represented in quantum memory systems (e.g., in a representation system that may represent materials using voxels or the like).
  • Multi-scale representations may also be employed that may be an aggregation of quantum and/or classical data at different scales stored on classical and/or quantum memory. These files may represent the bonds, dynamics of particles, interactions between them, the chemical, physical, electromagnetic or optical properties, and/or the like.
  • a spatial magnetic field map may be used to generate an electric field over a volume from which an electrochemical impedance spectroscopy may be used.
  • advanced impedance spectroscopy may be employed using quantum sensors (e.g., quantum impedance spectroscopy, quantum ‘MAIS’, and/or the like).
  • molecular or atomic structures may be mapped (e.g., via measuring shifts in probability distribution functions that are indicative of fundamental structure shift).
  • the output may be a classical timeseries.
  • Quantum Algorithms and Computing Based Methods [00419] The present disclosure is further directed to the use of quantum algorithms and/or computing based methods for matter and/or materials simulation, property prediction, materials selection, materials optimization, and/or materials generation (e.g., building materials such as concrete, cement or steel, as well as the matter in the quantum mechanical regime that such materials are made of, for example atoms, molecules, crystalline cells, amorphous quantum structures, particles such as electrons, protons, quarks and/or other such constituents of matter).
  • quantum-based methods herein may additionally or alternatively be used for material characterization, materials fingerprinting based methods for identifying materials based on quantum mechanical fingerprints, that may include building materials such as concrete mixes for mix fingerprinting.
  • quantum mechanical fingerprinting may include methods for identifying and/or categorizing a material based on material quantum mechanical properties and/or composition. Additionally, or alternatively, this may include methods that include using quantum sensors to monitor quantum mechanical properties, training a quantum or classical machine learning based algorithm or other algorithm to characterize, and the use of data generated from such sensors or metrology techniques as inputs or training data for any of the methods or systems described herein (including any machine learning model for material generation, optimization, design, adjustment or property prediction).
  • Quantum sensors may be utilized for material characterization, mix fingerprinting, sensor context awareness, and contextual condition determination.
  • Quantum algorithms disclosed herein may be executed on a classical computer, on a quantum computer, or on a hybrid quantum-classical-computer. They may also be distributed across a network that may include one or more of a quantum processing unit, central processing unit, graphical processing unit, or other dedicated machine-learning chips (e.g., neural processing unit).
  • Summary [00421]
  • fields associated with and/or in materials may be measured. Determining point measurements or spatial extents of fields within a material, in some cases, may include their time evolution or response to forces or impulses.
  • Embodiments of the present disclosure contemplate fields that may be scalar, vector or tensor fields.
  • Said fields may include, but are not limited to, electric fields, gravitational fields, magnetic field, thermal fields, displacement fields, strain or stress fields, and dielectric fields.
  • the fields described herein may be sensed by quantum technologies including magnetometers, electrometers, and gravimeters.
  • gradiometers may be used to measure a change in said properties (e.g., by exploiting quantum effects to increase accuracy via obtaining the delta or change on the 'quantum side' rather than classical side).
  • gravity gradiometry may be used to detect minute changes in gravitational fields that may be mapped back to a density of target materials.
  • Magnetometry and electrometry embodiments may include detecting confined quantum states (e.g., spin defect based (e.g., NV centers and silicon carbide) and quantum dots.
  • Quantum gravimetry embodiments may include Rydberg atoms and atom interferometry and atom clouds for super high resolution.
  • Light or quantum photonic embodiments may include squeezed, entangled, or correlated light for reducing noise and super resolution.
  • additional considerations may include noise and removal of background fields (e.g., via electromagnetic shielding, or via the use of control or reference sensors (e.g., self- detection or context awareness)).
  • Said quantum sensors may, in some embodiments, be placed in cavities or attached to frames, so that resonance signals and their shifts may be observed.
  • Embodiments of the present disclosure may spatially map any of the fields contemplated herein using arrays, or continuous field mapping techniques.
  • arrays may measure single volumes with spin defects etched in, array may include a plurality of individual quantum sensing elements interconnected, may include a plurality of dispositions and geometries, may be configured to be adaptive to sensed information, may be interconnect across additional arrays, and/or may generate data that may be processed (classically or quantumly).
  • a machine learning model may be used to enhance resolution beyond classical limit. Additionally, or alternatively, machine learning models may be used to reconstruct spatial maps of materials or fields. Further, quantum effects may be exploited to increase resolution beyond classical limit.
  • optics may be used in conjunction with any of the arrays described herein (e.g., quantum microscopes). Such embodiments may include microwave cavities, not just traditional photonic optics.
  • quantum sensing arrays may be used to remove background noise (e.g., given inverse square laws for most fields, material may be isolated from background fields from spatially distributed array). In such embodiments, shielding may not be needed as transient noise may be filtered out.
  • a plurality of confined quantum states e.g., spin defect based, such as NV center, or silicon carbide
  • a field may be applied onto a target material. In such embodiments, the field may be measured by entangled quantum states.
  • quantum states may be transferred from confined states to a photon, that may then be transmitted on a quantum channel to a quantum circuit (e.g., QPU, quantum gates, and/or the like). In some embodiments, this may be performed for each quantum sensing element in the array. Additionally, or alternatively, a QPU may execute a quantum algorithm to reconstruct a spatial map of the field that may then be output into classical memory. [00425] Embodiments of the present disclosure may include using wave-based sensing techniques including applying a time-varying field, force, and/or potential to a target material and measuring a response of the target material using a quantum sensor.
  • a quantum MAIS technique may be employed where frequency response of amplitude and phase shift is observed as a plurality of frequencies are swept through. This may apply to E or D fields, B or H fields, G fields and/or the like. All types of electromagnetic impedance as described herein may also be considered. In some embodiments, additional types of time- varying fields (e.g., impulse or step functions) may be employed. As will be understood by one of ordinary skill in the art in view of the present disclosure, this exploits the fact that the input field is known, and the 'control' is known (e.g., measurement of the sensor in a vacuum).
  • gravimetry may be used for density distribution mapping, air void mapping, and/or the like.
  • quantum photonics may be used as described herein for spectroscopy techniques that may provide data on structure and bonds (e.g., X-ray crystallography).
  • ultra cold atom clouds for continuous magnetic field mapping may be used for structural or material disposition determinations.
  • a model may be configured for material property prediction or simulation as described herein.
  • a model may be configured for material generation or design and discovery as described herein.
  • a non-exhaustive list of material generation or design and discovery models may include a model that generates one or a plurality of material compositions based on target objectives, a model that generates a plurality of material compositions continuously (e.g., discovery mode), and a model that generates one or a plurality of material production processes based on a desired material output.
  • a model may be configured for material production (MP) as described herein.
  • a non-exhaustive list of material production model may include predicting composition and/or properties of a material that goes through a production process based on the production process and the raw materials, a model that evaluates a set of production processes (or aspect of a production process) against a set of target objectives, and outputs an evaluation identifier, a model that optimizes one or a plurality of production processes based on target objectives and material output, and a model that generates one or a plurality of material production processes based on a desired material output.
  • a non-exhaustive list of material considered by embodiments of the present disclosure may include batteries, metal alloys, concrete, biomaterials, nanomaterial, polymers, and amorphous materials.
  • Figure 4 illustrates example configurations 400 for multi-modal quantum sensor arrays in accordance with some embodiments of the present disclosure.
  • the example configurations 400 may each include a plurality of multi-modal quantum elements 402 (MMQEs).
  • An MMQE 402 may include one or more quantum electrometers, one or more quantum magnetometers, and/or one or more quantum gravimeters that may function as described in the present disclosure.
  • an MMQE 402 may be coupled with a plurality of additional MMQEs.
  • the MMQE 402 may be coupled with a plurality of additional MMQEs in a two-dimensional configuration forming a two-dimensional MMQE array 404.
  • Figure 5 illustrates an example quantum sensing element 500 for an NV center diamond setup in accordance with some embodiments of the present disclosure.
  • the example quantum sensing element 500 may include a light source 502, one or more optical elements 506, an NV center in diamond 508, an electromagnetic antenna 510, and/or a photodetector 520.
  • the light source e.g., a laser
  • the emitted light 504 may interact with one or more optical elements 506 (e.g., beam splitters, filters, optical fibers, mirrors, and/or the like) to direct, control, analyze, and/or the like the emitted light 504.
  • optical elements 506 e.g., beam splitters, filters, optical fibers, mirrors, and/or the like
  • the emitted light 504 after interacting with the one or more optical elements 506, may be directed at the NV center in diamond 508.
  • the NV center in diamond 508 may include a nitrogen atom 512 and an adjacent vacancy 514 such that the NV center in diamond 508 may be configured as a quantum system that may interact with external fields.
  • an external field may be applied to the NV center in diamond 508 via the electromagnetic antenna 510.
  • properties (e.g., spin) of the NV center in diamond 508 may be manipulated and/or influenced by the external field.
  • the emitted light 504 may interact (e.g., electron excitation) with the nitrogen- vacancy center of the NV center in diamond 508.
  • a fluorescence emission 518 may be emitted from the NV center in diamond 508 (e.g., due to energy state transitions). Additionally, or alternatively, the fluorescence emissions 518 may be detected by the photodetector 520. It will be understood that the present disclosure contemplates setups for quantum sensing elements for NV center in diamond configurations including additional elements (e.g., sensors, devices, optical elements, and/or the like), fewer elements, interchanged elements, different orders of elements, and/or the like. [00439] Figure 6 illustrates an example method 600 for a sensing array in proximity with a target material in accordance with some embodiments of the present disclosure.
  • a target material 602 may be in proximity to, may be emitting, and/or may be interacting with a field 604 (e.g., a magnetic field).
  • a quantum sensor array 606 (e.g., similar to the example configurations 300 and the example configurations 400 as shown and described herein with respect to Figures 3 and 4, respectively) may include a plurality of quantum sensors.
  • the quantum sensor array 606 may be configured to detect and/or measure properties associated with the field 604 (e.g., strength, changes in quantum states, and/or the like). Further the quantum sensor array 606 may be configured to make a plurality of measurements in varying spatial locations and/or varying points in time.
  • the quantum sensor array 606 may be configured to sense magnetic field lines of the field 604.
  • the quantum sensor array 606 may be configured to output their measurements and/or detections to one or more AI models 608 (e.g., a material prediction model as described herein).
  • the one or more AI models 608 may be configured to analyze the output of the quantum sensor array 606 and generate a material and/or quantum material characterization 610.
  • the output of the quantum sensor array 606 may be ingested by the one or more AI models 608 configured as a material prediction model that outputs a material property, composition, and/or disposition spatial map (e.g., reconstruct an image of the target material based on a sensed field).
  • Figure 6 may include additional models and/or additional model configurations to output a plurality of material and/or quantum material characterizations 610. Further, Figure 6 may include additional or fewer steps and/or additional or fewer devices.
  • Figure 7 illustrates an example method for reconstructing a three-dimensional target material using a sensor array in accordance with some embodiments of the present disclosure.
  • a target material 702 may be in proximity to, may be emitting, and/or may be interacting with a field 704 (e.g., a magnetic field).
  • a quantum sensor array 706 e.g., similar to the example configurations 300 and the example configurations 400 as shown and described herein with respect to Figures 3 and 4, respectively including a plurality of quantum sensors.
  • the quantum sensor array 706 may be configured to detect and/or measure properties associated with the field 704 (e.g., strength, changes in quantum states, and/or the like). Further the quantum sensor array 706 may be configured to make a plurality of measurements in varying spatial locations and/or varying points in time. As an example, the quantum sensor array 706 may be configured to sense magnetic field lines of the field 704. In some embodiments, the quantum sensor array 706 may be configured to output their measurements and/or detections to a first model 708 (e.g., a field reconstruction model). Further, the first model 708 may be configured to analyze the output of the quantum sensor array 706 and generate a three-dimensional reconstruction 710 of the field 704 within a volume of the target material 702.
  • a first model 708 e.g., a field reconstruction model
  • the quantum sensor array 706 may sense magnetic field lines of the field 704 associated with the target material 702 and output the measurements for ingestion by the first model 708 configured to generate a three-dimensional reconstruction 710 of the field 704 within a volume of the target material 702. Additionally, or alternatively, the output of the first model 708 may be fed into a second model 712 (e.g., a material prediction model as described herein) configured to analyze the three-dimensional reconstruction 710 and output a material property, composition, and/or disposition spatial map 714 (e.g., reconstruct an image of the target material based on the three-dimensional reconstruction 710).
  • Figure 7 may include additional models and/or additional model configurations to output a plurality of material and/or quantum material characterizations 714.
  • a quantum sensor 808 may have a plurality of adjacent quantum sensors separated by distances.
  • a first adjacent quantum sensor to the quantum sensor 808 may be separated by a first distance and a second adjacent quantum sensor to the quantum sensor 808 may be separated by a second distance.
  • the first distance and the second distance may be equivalent.
  • the first distance and the second distance may differ.
  • a first distance and a second distance of a different quantum sensor may be equivalent to and/or different than the first distance and the second distance of the quantum sensor 808.
  • additional distances may be used for the quantum sensor 808 and adjacent quantum sensors (e.g., higher dimensionality arrays).
  • the example configurations 1000 may include a three-dimensional quantum sensor and classical actuator array 1008, where the three-dimensional quantum sensor and classical actuator array 1008 includes a plurality of quantum sensing elements 1004 and/or a plurality of classical actuators 1006 configured to sense and/or measure a plurality of material properties (e.g., vie the quantum sensing elements 1004) and/or apply a field, a force, a potential, and/or the like to a target material and/or an environment surrounding the target material (e.g., via the classical actuators 1006).
  • Figure 10 illustrates a two-dimensional and a three-dimensional embodiment, the present disclosure contemplates different dimensionality embodiments and/or different structures of quantum sensor and classical actuator configurations.
  • the measurements and/or reconstructions 1108 of the field 1102 may be input into a determination model 1110 to generate complex impedances 1112 (e.g., electromagnetic wave impedance, electrochemical impedance, and/or the like) at one of and/or a plurality of locations (e.g., x,y,z locations) and/or volumes within the target material 1102.
  • the determination model 1110 may be configured as a material prediction model as described herein.
  • the determination model 1110 may output the complex impedances 1112 at one of and/or a plurality of locations and/or volumes within the target material 1102 (e.g., as seen in a graph 1116 of an impedance magnitude plot for one position).
  • the method 1100 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein.
  • Figure 11 shows example elements and example steps of the method 1100, in some embodiments, the method 1100 may include additional elements and/or steps, fewer elements and/or steps, different elements and/or steps, or differently arranged elements and/or steps than those depicted in Figure 11.
  • Figure 12 illustrates example circuitries 1200 for quantum sensor and classical actuator arrays in accordance with some embodiments of the present disclosure.
  • the circuitry 1200 may include a classical CPU configuration 1202 that may include a plurality of filters (e.g., noise filters), a plurality of quantum sensing elements, one or more classical data channels, at least one CPU, and/or at least one time synchronization module.
  • the circuitry 1200 may include a quantum QPU configuration 1204 that may include a plurality of filters (e.g., noise filters), a plurality of quantum sensing elements, one or more quantum data channels, at least one QPU, and/or a quantum time clock or time synchronization module.
  • the circuitry 1200 may include a hybrid quantum-classical system 1206 that may include a plurality of quantum sensing elements, one or more classical data channels, one or more quantum data channels, one or more hybrid channels, at least one CPU operably coupled with a time synchronization module, and/or at least one QPU operably coupled with a time synchronization module.
  • the plurality of quantum sensing elements may sense and/or measure a target material, the measurement may be output to the plurality of noise filters to manipulate the output, the output may be transmitted via the one or more classical channels and/or the one or more quantum channels to the at least one CPU and/or the at least one QPU for processing.
  • the example circuitries 1200 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein. Although Figure 12 shows example circuitries 1200, in some embodiments, the circuitries 1200 may include additional elements, fewer elements, different shapes, or differently arranged elements than those depicted in Figure 12. [00448]
  • Figure 13 illustrates a diagram for a system 1300 including processing units and quantum sensors and classical actuator arrays in accordance with some embodiments of the present disclosure.
  • the system 1300 may include a plurality of quantum sensing elements 1304 each operably coupled to a classical and/or quantum channel, a plurality of classical actuators 1302 (e.g., electromagnetic antennas) each operably coupled to a classical channel, a microcontroller unit (MCU) 1306 including a plurality of processing units 1308 (e.g., a GPU, CPU, QPU, and/or the like), and a clock 1310 (e.g., a classical clock, a quantum clock, and/or the like).
  • MCU microcontroller unit
  • processing units 1308 e.g., a GPU, CPU, QPU, and/or the like
  • a clock 1310 e.g., a classical clock, a quantum clock, and/or the like.
  • the plurality of quantum sensing elements 1304 and/or the plurality of classical actuators 1302 may be configured to transmit and/or receive data via their associated channels from the MCU 1306.
  • the plurality of processing units 1308 may be configured to control the plurality of quantum sensing elements 1304 and/or the plurality of classical actuators 1302 and/or may be configured to collect and interpret data from said elements.
  • the system 1300 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein. Although Figure 13 shows the system 1300 including processing units and quantum sensors and classical actuator arrays, in some embodiments, the system 1300 may include additional elements, fewer elements, different shapes, or differently arranged elements than those depicted in Figure 13. [00449] Figure 14 illustrates an example material sheet with a substrate measurement layer 1400 in accordance with some embodiments of the present disclosure.
  • the material sheet with a substrate measurement layer 1400 may include a sheet of sensing material 1402 and a substrate measurement layer 1404.
  • the sheet of sensing material 1402 may, in some embodiments, include a sheet of diamond.
  • the sheet of diamond may include spin defects (e.g., NV center in diamond), quantum dots, and/or the like.
  • the substrate measurement layer 1404 may include a plurality of light sources (e.g., lasers), a plurality of photodetectors, a plurality of electromagnetic antennas, and/or additional miniaturized chips and/or circuitry.
  • the sheet of sensing material 1402 may be coupled to the substrate measurement layer 1404 such that the spin defects and/or quantum dots of the sheet of sensing material 1402 may interact with and be measured and/or sensed by elements of the substrate measurement layer 1404.
  • the plurality of electromagnetic antennas may emit electromagnetic waves to manipulate quantum states of spin defects and the plurality of light sources may emit a light to interact with the spin defects such that electrons may be excited and deexcited to emit fluorescence that may be sensed and/or detected by the plurality of photodetectors.
  • Figure 15 illustrates an example quantum sensor and classical actuator array configuration 1500 in accordance with some embodiments of the present disclosure.
  • the quantum sensor and classical actuator array 1500 may include a frame 1508, where a first side of the frame 1508 includes a quantum sensing element and classical actuator array 1502 and a second side of the frame 1508 opposite the first side includes a sensor actuator array unit 1506.
  • the frame 1508 may include a space between the first side and the second side such that a target material 1504 may be placed in between the quantum element and classical actuator array 1502 and the sensor actuator array unit 1506.
  • the target material may be sensed and/or measured by the sensing units and properties of the target material may be influenced by the actuators, where the influenced properties of the target material may be further measured by the sensing units.
  • the frame 1508 may have a first portion including the sensor a 'portions of a frame' / different areas or volumes in relation to such frames.
  • the quantum sensor and classical actuator array 1500 may include the frame 1508, where a first portion (e.g., an area or volume) of the frame 1508 includes a quantum sensing element and classical actuator array 1502 and a second portion of the frame 1508 opposite the first side includes a sensor actuator array unit 1506.
  • the frame 1508 may include a space between the first portion and the second portion such that a target material 1504 may be placed in between the quantum element and classical actuator array 1502 and the sensor actuator array unit 1506.
  • Figure 16 illustrates an example quantum sensor and classical actuator array configuration 1600 in accordance with some embodiments of the present disclosure.
  • the example quantum sensor and classical actuator array configuration 1600 may be a device 1602 that includes a quantum sensor and classical actuator array and a cavity or a frame.
  • the device 1602 may be spherical, cylindrical, rectangular, and/or the like in shape.
  • the cavity or the frame of the device 1602 may be spherical, cylindrical, rectangular, and/or the like in shape.
  • the quantum sensor and classical actuator array may be embedded in, on a surface of, and/or coupled to the device 1602.
  • the device 1602 is cylindrical with a cylindrical cavity in the center of the device 1602.
  • the quantum sensor and classical actuator array is structured such that it surrounds the cylindrical cavity or the frame of the device 1602.
  • the quantum sensor and classical actuator array may be fully embedded in the device 1602, may be disposed on walls of the cavity of the device 1602, and/or a combination of both.
  • a target material may be positioned in the cavity or the frame (e.g., placed, poured, and/or the like) such that the quantum sensor and classical actuator array may sense the initial target material, apply a force, field, potential, and/or the like to the target material, and/or sense the actuated target material.
  • quantum sensing enables detection of peaks below the classical noise limit, and also enables super-resolution of the target material.
  • the example quantum sensor and classical actuator array configuration 1600 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein.
  • one or more quantum sensors may be used in the cavity and/or disposed around the frame or one or more actuators in a cavity or disposed around a frame (or at least one quantum sensor and one actuator). Said embodiments may lead to resonance peaks that may be detected with the one or more quantum sensors.
  • Figure 16 shows an example device 1602
  • the device 1600 may include additional elements, fewer elements, different shapes, or differently arranged elements than those depicted in Figure 16.
  • Figure 17 illustrates a quantum photonic device 1700 embodiment in accordance with some embodiments of the present disclosure.
  • components 1702 of the quantum photonic device 1700 may include a quantum control device 1704, one or more quantum sensors 1706, and/or a quantum illuminator 1708.
  • the quantum control device 1704 may include a MCU including a plurality of processing units (e.g., CPU, GPU, QPU, NPU, and/or the like), a power module, a wireless communication models, and/or a plurality of channels (e.g., classical channels, quantum channels, and/or hybrid channels) configured to couple the quantum control device 1704 to the one or more quantum sensors 1706.
  • the one or more quantum sensors 1706 may include a quantum sensor controller operably coupled via the plurality of channels to quantum control device 1704 (e.g., coupled to the MCU).
  • the quantum illuminator 1708 may include a quantum illuminator controller operably coupled via the plurality of channels to the quantum control device 1704 (e.g., coupled to the MCU).
  • a first subset of the plurality of channels may operably couple the quantum control device 1704 to the one or more quantum sensors 1706 and/or a second subset of the plurality of channels may operably couple the quantum control device 1704 to the quantum illuminator 1708.
  • the quantum illuminator 1708 may be configured to generate entangled states and/or squeezed states.
  • Graphs 1710 show examples of entangled states and fringe interference in accordance with some embodiments of the present disclosure.
  • oscillatory features may be observed in Wigner state diagrams (e.g., fringes) that may arise from quantum interference generated by superposition of states with varying phase relationships.
  • fringes may become progressively sharper and more densely spaced, facilitating a greater sensitivity to phase variations.
  • fringe interference patterns may arise in whole or in part from the phase shift between entangled states, which may be used by the quantum system to enable super resolution beyond the classical diffraction limit.
  • entangled quantum states may enable phase sensitivity that scales with 1/N, surpassing the classical threshold of 1/sqrt( ⁇ ).
  • This enhanced sensitivity encodes intricate phase information, allowing for the detection of perturbations with significantly improved precision and detail.
  • the spacing of interference fringes is inversely proportional to N, enabling resolutions far beyond what classical diffraction limits permit.
  • the quantum photonic device 1700 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein.
  • Figure 17 shows example components 1702 of the quantum photonic device 1700
  • the quantum photonic device 1700 may include additional elements, fewer elements, different shapes, or differently arranged elements than those depicted in Figure 17.
  • Figure 18 illustrates another quantum photonic device 1800 embodiment in accordance with some embodiments of the present disclosure.
  • components 1802 of the quantum photonic device 1800 may include an exotic light state generation module including a photon source and an optical nonlinear material, an MCU, a photodetector and/or spectrum detector, a quantum illumination source, and/or a lens.
  • the MCU may control the exotic light state generation module and may dynamically determine a squeezed state of light to generate next during operation (e.g., based on previously generated sensor data).
  • the quantum photonic device 1800 may adaptively adjust the levels of squeezing and/or entanglement due to what is happening during operation and/or based on set requirements. For example, the quantum photonic device 1800 may obtain a first spectral measurement, identify the presence or the lack of features within a particular range of the spectral measurement (e.g. lack of a peak when one was expected, or aliasing), make a change to the squeezing parameters (e.g.
  • an exotic state generated by the exotic light state generation module may be a squeezed state, an entangled state, and/or other special quantum light states.
  • the exotic light state generation module may generate a state in whole or in part using parametric down-conversion.
  • the quantum illumination source may be configured to emit the quantum light states prepared by other components 1802 of the quantum photonic device 1800. Further, the quantum illumination source and the lens may, optionally, be separated by a gap (e.g., air, vacuum, and/or the like).
  • the MCU may include a CPU or a CPU controller unit, a QPU to read out quantum measurements, and/or a time synchronization module (e.g., similar to the MCU 1306 as shown and described herein with respect to Figure 13).
  • Graphs 1810 show examples of Wigner state diagrams for squeezed light state embodiments.
  • the graphs 1810 of Wigner state diagrams for squeezed light states provide visual representation of quantum states in phase space, showing the squeezing of uncertainty in one quadrature and the corresponding increase in the conjugate quadrature.
  • this behavior may manifest as an elliptical distribution with negative regions indicating non-classical behavior.
  • the Wigner function may show a displaced elliptical shape, reflecting the squeezing and the coherent displacement.
  • the operations illustrated in Figure 19 may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., the server 200 as shown and described herein with respect to Figure 2), as described above.
  • performance of the operations may invoke one or more of processor 202, memory 206, communication interface 204, and/or artificial intelligence (AI) module 208 as shown and described herein with respect to Figure 2.
  • AI artificial intelligence
  • the operations illustrated in Figure 19 may be performed, either fully or in part, by one or more quantum devices.
  • the flowchart 1900 may include the step of interacting one or more quantum states with a target material.
  • the interaction between the one or more quantum states and the target material may refer to a process in which a system associated with the one or more quantum states exchanges energy, momentum, information, and/or the like with the target material.
  • the interaction may result in a change to one or more quantum states of the one or more quantum states.
  • the interaction may lead to phenomena including energy absorption or emission, spin manipulation, collapse of a wavefunction or quantum state, and/or any other phenomena described herein.
  • the memory device may include one or more databases including data on the target material and/or additional materials. Additionally, and/or alternatively, the one or more first data elements may be received as input into a processing device, and/or an AI model for use in further processing, simulating, data manipulation, and/or the like (e.g., the first model as shown and described herein with respect to Figure 22).
  • the flowchart 1900 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein. Although Figure 19 shows example blocks of the flowchart 1900, in some embodiments, the flowchart 1900 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 19.
  • FIG. 20 illustrates a flowchart 2000 for another example method for a quantum material-related characterization model, in accordance with some embodiments of the present disclosure.
  • the operations illustrated in Figure 20 may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., the server 200 as shown and described herein with respect to Figure 2), as described above.
  • the flowchart 2000 may include the step of generating one or more first data elements associated with the measured frequency dependencies of field measurements.
  • the one or more first data elements may include output of one or more sensor devices (e.g., a classic sensor, a quantum sensor, and/or a hybrid sensor) configured to measure properties of the target material associated with frequency dependencies of field measurements.
  • the one or more first data elements associated with the frequency dependence of the target material’s response to an applied field may help characterize the target material’s properties and behavior (e.g., dielectric properties, magnetic properties, conductivity, relaxation processes, and/or any other material property as described in the present disclosure). Said data may serve to characterize the target material, optimize the target material, evaluate the target material, and/or the like.
  • the flowchart 2000 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein. Although Figure 20 shows example blocks of the flowchart 2000, in some embodiments, the flowchart 2000 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 20.
  • Figure 21 illustrates a flowchart 2100 for another example method for a quantum material-related characterization model, in accordance with some embodiments of the present disclosure.
  • the operations illustrated in Figure 21 may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., the server 200 as shown and described herein with respect to Figure 2), as described above.
  • the flowchart 2100 may include the step of determining a change in at least one quantum state of one or more quantum states after an interaction with a target material.
  • a quantum state may be a description that encapsulates information of a system and its properties and behaviors in quantum mechanics (e.g., representations of probabilities of different outcomes for measurements on a system, such as position, momentum, spin, or energy).
  • the interaction may lead to phenomena including energy absorption or emission, spin manipulation, collapse of a wavefunction or quantum state, and/or any other phenomena described herein.
  • the target material may serve as a medium that may influence and/or reveal properties of the one or more quantum states. For example, using processes including scattering, coupling, decoherence, and/or any other quantum interaction process described in the present disclosure.
  • a change in the at least one quantum state post interaction with the target material may be detected and the change may be determined, and the change may be used to characterize the target material.
  • the step of block 2102 may be performed following the step of block 1904 as shown and described herein with respect to Figure 19.
  • the flowchart 2100 may include the step of generating one or more second data elements associated with a characteristic of the target material based on the determined change in the at least one quantum state.
  • the one or more second data elements generated by the determined change in the at least one quantum state after the interaction with the target material may include measurements of the energy, momentum, position, spin, phase, optical properties, thermodynamic properties, charge transport, and/or other properties as described herein. Said measurements may reveal information about both the at least one quantum state and the target material including structure, composition, quantum coherence, and/or the like.
  • the generated one or more second data elements may depend on interaction type (e.g., if the interaction is scattering, absorption, measurement, entanglement, and/or any others described herein). Further, the one or more second data elements may include one or more material identifiers, a plurality of material composition data, a plurality of material properties, and/or material properties in a plurality of contextual conditions associated with the target material. [00474] As shown in block 2106, the flowchart 2100 may include the step of outputting the one or more second data elements. In some embodiments, the one or more second data elements may be transmitted to a memory device for short-term and/or long-term storage.
  • the memory device may include one or more databases including data on the target material and/or additional materials. Additionally, and/or alternatively, the one or more second data elements may be received as input into a processing device, and/or an AI model for use in further processing, simulating, data manipulation, and/or the like.
  • the flowchart 2100 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein. Although Figure 21 shows example blocks of the flowchart 2100, in some embodiments, the flowchart 2100 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 21. Additionally, or alternatively, two or more of the blocks of the flowchart 2100 may be performed in parallel.
  • the flowchart 2200 may include the step of inputting one or more first data elements into a first model configured to generate a first output, where the first output is associated with a property of a target material.
  • the one or more first data elements may include data generated based on measurements associated with an interaction of one or more quantum states with a target material (e.g., as shown and described herein with respect to Figure 19).
  • FIG. 23 illustrates a flowchart 2300 for another example method for a quantum material-related characterization model, in accordance with some embodiments of the present disclosure.
  • the operations illustrated in Figure 23 may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., the server 200 as shown and described herein with respect to Figure 2), as described above.
  • the flowchart 2300 may include the step of measuring one or more third data elements from the one or more sensors.
  • the one or more sensors may be configured to output data related to associated properties associated with each sensor of the one or more sensors (e.g., a temperature sensor outputs temperature measurements, a pressure sensor outputs pressure measurements, a quantum magnetometer outputs magnetic field measurements, and the like).
  • the one or more third data elements may include the outputted data of the one or more sensors.
  • a plurality of data regarding the target material and the environment surrounding the target material may be generated and included in the one or more third data elements.
  • the one or more third data elements may be used to adjust other measurements made by additional sensors (e.g., as shown and described herein within respect to Figure 25), as input to an AI model for characterization of the target material (e.g., as shown and described herein with respect to Figure 24), as input to train an AI model, and/or for other uses as described herein.
  • the flowchart 2300 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein.
  • Figure 23 shows example blocks of the flowchart 2300
  • the flowchart 2300 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 23. Additionally, or alternatively, two or more of the blocks of the flowchart 2300 may be performed in parallel.
  • the steps of the flowchart 2300 may include and/or accomplished by any other the quantum techniques and/or devices as described herein.
  • Figure 24 illustrates a flowchart 2400 for another example method for a quantum material-related characterization model, in accordance with some embodiments of the present disclosure.
  • the operations illustrated in Figure 24 may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., the server 200 as shown and described herein with respect to Figure 2), as described above.
  • performance of the operations may invoke one or more of processor 202, memory 206, communication interface 204, and/or artificial intelligence (AI) module 208 as shown and described herein with respect to Figure 2.
  • AI artificial intelligence
  • the operations illustrated in Figure 24 may be performed, either fully or in part, by one or more quantum devices.
  • the flowchart 2400 may include the step of inputting one or more third data elements into a first model.
  • the one or more third data elements may include measurements from one or more sensors embedded on, directed at, coupled to, and/or mounted on a target material and/or an environment surrounding the target material (e.g., similar to the step of block 2304 as shown and described herein with respect to Figure 23). Further, the step of block 2402 may be performed in conjunction with and/or may follow the step 2304 of the flowchart 2300 as shown and described herein with respect to Figure 23. Additionally, or alternatively, the first model may be an AI model configured to predict, simulate, optimize, generate, evaluate, and/or function as any of the model methods described herein a target material and/or properties of a target material.
  • the step of block 2402 may be performed in conjunction with and/or may follow the step of block 2202 as shown and described herein with respect to Figure 22.
  • the flowchart 2400 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein.
  • Figure 24 shows example blocks of the flowchart 2400, in some embodiments, the flowchart 2400 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 24. Additionally, or alternatively, two or more of the blocks of the flowchart 2400 may be performed in parallel.
  • Figure 25 illustrates a flowchart 2500 for another example method for a quantum material-related characterization model, in accordance with some embodiments of the present disclosure.
  • the operations illustrated in Figure 25 may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., the server 200 as shown and described herein with respect to Figure 2), as described above.
  • an apparatus e.g., the server 200 as shown and described herein with respect to Figure 2
  • performance of the operations may invoke one or more of processor 202, memory 206, communication interface 204, and/or artificial intelligence (AI) module 208 as shown and described herein with respect to Figure 2. Additionally, or alternatively, the operations illustrated in Figure 25 may be performed, either fully or in part, by one or more quantum devices.
  • the flowchart 2500 may include the step of using one or more third data elements to adjust a quantum magnetometry measurement, a quantum gravimetry measurement, and/or a quantum electric field measurement.
  • the one or more third data elements may include output from one or more classical sensors, one or more quantum sensors, and/or one or more hybrid sensors (e.g., the one or more third data elements of block 2304 as shown and described herein with respect to Figure 23).
  • a target material, a material coupled to the target material, and/or an environment including the target material that includes the one or more sensors may be associated with the quantum magnetometry measurement, the quantum gravimetry measurement, and/or the quantum electric field measurement.
  • the one or more third data elements may be used to adjust said measurements to compensate for environmental conditions, sensor limitations, noise, and/or the like.
  • the quantum magnetometry measurement may be adjusted with measurements of inertial sensors, temperature sensors, optical sensors, and/or the like (e.g., the one or more third data elements) to enhance the accuracy and/or precision of a magnetic field measurement of a target material.
  • the present disclosure contemplates any single device or combination of devices described herein to adjust quantum magnetometry, quantum gravimetry, and/or quantum electric field measurements.
  • the step of block 2502 of the flowchart 2500 may be performed prior to, in conjunction with, and/or following the steps of flowchart 2300 as shown and described herein with respect to Figure 23.
  • the flowchart 2500 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein.
  • Figure 25 shows example blocks of the flowchart 2500
  • the flowchart 2500 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 25. Additionally, or alternatively, two or more of the blocks of the flowchart 2500 may be performed in parallel.
  • the steps of the flowchart 2500 may include and/or accomplished by any other the quantum techniques and/or devices as described herein.
  • Figure 26 illustrates a flowchart 2600 for another example method for a quantum material-related characterization model, in accordance with some embodiments of the present disclosure.
  • the operations illustrated in Figure 26 may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., the server 200 as shown and described herein with respect to Figure 2), as described above.
  • performance of the operations may invoke one or more of processor 202, memory 206, communication interface 204, and/or artificial intelligence (AI) module 208 as shown and described herein with respect to Figure 2.
  • AI artificial intelligence
  • the operations illustrated in Figure 26 may be performed, either fully or in part, by one or more quantum devices.
  • the step of block 2602 of the flowchart 2600 may be performed prior to, in conjunction with, and/or following the steps of flowchart 2300 as shown and described herein with respect to Figure 23.
  • the flowchart 2600 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein.
  • Figure 26 shows example blocks of the flowchart 2600, in some embodiments, the flowchart 2600 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 26. Additionally, or alternatively, two or more of the blocks of the flowchart 2600 may be performed in parallel.
  • the steps of the flowchart 2600 may include and/or accomplished by any other the quantum techniques and/or devices as described herein.
  • Materials Crystals and Semiconductor Structures [00492]
  • materials may be described (for example to be either inputted into the models herein or outputted by the models herein) using a material identifier.
  • material identifiers may be data indicative of material type, or it may be a name or material ID. Additionally or alternatively, the material type may be a crystalline material.
  • composition of crystalline materials may be described using composition data.
  • composition data for a crystalline material may include chemical composition data, as well as structural data.
  • composition data for a crystalline material may further include chemical composition data including any of species of the atom, atomic number, atomic radius, valence electron data, electronegativity, valence electron configuration, atomic mass, bond type e.g. covalent, ionic, and/or the like, electron affinity, ionization energy, stoichiometry, and bond valence sum.
  • compositions may be embedded or encoded as inputs into the models herein.
  • lattice representations as grid points in space
  • Bravais lattice representations e.g., cubic, tetragonal, hexagonal, miller indices (h,k,l)
  • space groups e.g., electron density maps, reciprocal lattice representations, distribution functions, radial distribution functions, Voronoi tessellations, and multi-scale representations that may include any of the above at a plurality of different orders of magnitude of volumes or lengths.
  • the composition data and lattice representations herein may be embedded or encoded as inputs into the models herein.
  • Fourier based representations may be used, for example to encode information regarding periodicity of the crystal structure. This may include reciprocal lattice-based representations as well, wherein k-vectors are used to represent lattice structures, and associated wavenumber and momentum related properties.
  • a graphical representation of the unit cell may be inputted into the models herein.
  • distributions may be used to represent the crystal, its constituent atoms, and structure. Additionally, or alternatively, this may be a radial distribution function. Additionally, or alternatively, this may be a partition function, which describes energetic state distributions.
  • the models herein may be trained to construct optimal descriptors or material identifiers from multi-parameter composition data (e.g. extracting data and constructing a material identifier from a graphical representation of a unit cell). Additionally optimal descriptors may be described as those which minimize the loss between the true value of a desired output, and the output generated by the model during training.
  • crystals may be represented through physico-chemical theoretical models. This may include quantum-based models, as well as DFT based models and simulations.
  • Further representations may include atomic cluster expansions, finite element models, and the like.
  • doping which may be introduced into a crystal structure, may be represented as inputs or outputs for the models herein. Additionally, or alternatively, this may include substitutional doping, interstitial doping, vacancy doping or any other type of doping. In some embodiments, doping may be represented for / by the models herein in a number of ways including: [00512] The models herein may use a supercell representation, which may be defined as an extension of the unit cell which adds more atoms or more ‘unit cells’. Dopants may be introduced in these modeled extended structures, at the relevant positions, and in the relevant quantities/ratios.
  • substitutional doping may involve replacing an atom by a dopant atom in the model. Additionally, or alternatively, representing interstitial doping may involve inserting a dopant atom in between lattice positions in the supercell representation. Additionally, or alternatively, vacancies may be represented by removing atoms in the supercell representation . [00514] Additionally, or alternatively, doping may be represented as a scalar, vector or tensor representing dopant concentration. [00515] In the context of semiconductors, crystalline structures have particular importance. In some embodiments, particular crystal types may be considered in the models herein. In some embodiments, crystal types of particular relevance for semiconductors which may be used in the context of the models herein may include the following.
  • the models herein may predict properties, simulate, evaluate, optimize, generate, discover, or generate or optimize production processes for any of these: [00516] Some further embodiments may include graphene analogues, as well as layered heterostructures which include a number of different, stacked and layered 2D structures with different properties. [00517] Some further embodiments may include III-V (e.g., AlN, GaN, InGaN) and II-VI Compounds (e.g., ZnO, CdTe), as well as mixed phase materials such as structures with hybrid zinc blende or wurtzite phases, or rhombohedral and hexagonal structures.
  • III-V e.g., AlN, GaN, InGaN
  • II-VI Compounds e.g., ZnO, CdTe
  • mixed phase materials such as structures with hybrid zinc blende or wurtzite phases, or rhombohedral and hexagonal structures.
  • Ternary and Quaternary Compounds [00519] For example, compounds of the form AxByCz and/or AwBxCyDz.
  • Perovskites [00521] Some further embodiments may include hybrid organic-inorganic perovskites.
  • Topological Insulator Crystal Structures [00523] In some embodiments, these may include layered topological insulators, quantum spin Hall insulators, and/or the like.
  • Superconductor Crystal Structures [00525] In some embodiments, these may include layered cuprates, iron-based superconductors, and/or the like.
  • 1D (line) Crystals and 0D (point) Crystals [00527] In some embodiments, these may include nanowires, nanotubes, nanoribbons, quantum dots, and/or the like. [00528] Additionally, or alternatively, the models and methods herein may be applicable to any crystalline structure and structure type, including but not limited to the following: Cubic (Simple), Cubic (Body-Centered), Cubic (Face-Centered), Tetragonal (Simple), Tetragonal (Body-Centered), Orthorhombic (Simple), Orthorhombic (Base-Centered), Orthorhombic (Body-Centered), Orthorhombic (Face-Centered), Hexagonal (Simple), Trigonal (Rhombohedral), Monoclinic (Simple), Monoclinic (Base-Centered), Triclinic (Simple), Diamond Cubic, Zincblende (Cubic), Wurtzite (Hex
  • crystals which may be considered by the models herein may include crystals such as: Quartz (SiO2), Diamond (C), Graphite (C), Silicon (Si), Gallium Nitride (GaN), Gallium Arsenide (GaAs), Sapphire (Al2O3), Zinc Oxide (ZnO), Calcium Fluoride (CaF2), Silicon Carbide (SiC), Titanium Dioxide (TiO2) – Rutile, Anatase, Brookite, Sodium Chloride (NaCl) – Rock Salt, Lithium Niobate (LiNbO3), Barium Titanate (BaTiO3), Perovskite (CaTiO3), Lead Zirconate Titanate (PZT), Magnesium Oxide (MgO), Potassium Bromide (KBr), Spinel (MgAl2O4), Indium Phosphide (InP), Lanthanum Aluminate (LaAlO4), Indium Phosphi
  • the models and methods herein to crystals of any chemical composition including any combination of elements and ratios of elements in the periodic table which may be used to construct a material: Hydrogen, Helium, Lithium, Beryllium, Boron, Carbon, Nitrogen, Oxygen, Fluorine, Neon, Sodium, Magnesium, Aluminum, Silicon, Phosphorus, Sulfur, Chlorine, Argon, Potassium, Calcium, Scandium, Titanium, Vanadium, Chromium, Manganese, Iron, Cobalt, Nickel, Copper, Zinc, Gallium, Germanium, Arsenic, Selenium, Bromine, Krypton, Rubidium, Strontium, Yttrium, Zirconium, Niobium, Molybdenum, Technetium, Ruthenium, Rhodium, Palladium, Silver, Cadmium, Indium, Tin, Antimony, Tellurium, Iodine, Xenon, Cesium, Barium, Lanthanum, Cerium,
  • mechanical properties may include hardness, elastic moduli (Young’s, bulk, shear moduli), tensile strength, fracture toughness, Poisson’s ratio, yield strength, compressive strength, flexural strength, ductility, toughness, fatigue strength, impact resistance, creep resistance, anisotropy factor (mechanical), brittleness index, stress-strain behavior, elastic limit, modulus of resilience, modulus of toughness, and/or the like.
  • thermal properties may include thermal conductivity, thermal expansion coefficient, specific heat capacity, thermal diffusivity, Debye temperature, melting point, boiling point, latent heat of fusion, latent heat of vaporization, heat capacity, thermal shock resistance, coefficient of thermal diffusion, temperature stability, thermal resistivity, glass transition temperature, pyroelectric coefficient, heat of sublimation, and/or the like.
  • optical properties may include refractive index, optical absorption coefficient, photoluminescence, transmittance/transparency, band edge absorption, reflectance, emissivity, extinction coefficient, Raman shift, birefringence, second harmonic generation coefficient, electro-optic coefficient, absorption spectrum, photoconductivity, fluorescence yield, nonlinear optical coefficients, optical bandwidth, absorption edge, and/or the like.
  • surface and defect properties may include surface energy, defect concentration, work function, surface tension, grain boundary energy, dislocation density, vacancy concentration, Frenkel defects, Schottky defects, twin boundaries, surface roughness, electron work function, surface charge density, interfacial energy, surface diffusion, adhesion energy, surface conductivity, catalytic activity (surface), step edge density, adsorption energy, and/or the like.
  • chemical reactivity and stability properties may include oxidation resistance, chemical stability, corrosion resistance, reduction potential, chemical durability, hydrolysis resistance, photochemical stability, solubility product, redox potential, chemical reactivity (with acids, bases, etc.), reaction enthalpy, corrosion rate, passivation layer thickness, decomposition temperature, resistance to hydrogen embrittlement, stability in different pH, electrochemical stability, stability under radiation, and/or the like.
  • sustainability properties may include embodied carbon, carbon footprint, embodied energy, resource availability, recyclability, toxicity, environmental impact, water footprint, material degradation and longevity, and/or the like.
  • production or manufacturing properties or data may include synthesis complexity, scalability, growth rate, growth conditions, nucleation, purity requirements, production cost, manufacturing yield / efficiency, raw material availability, process reproducibility, processing time, automation potential, and/or the like.
  • properties of particular interest which the models herein may ingest or predict include bandgap energy, carrier mobility, thermal conductivity, dielectric constant, recombination rate, electrical conductivity, breakdown voltage, thermal expansion coefficient, electron affinity, optical absorption coefficient, refractive index, saturation velocity, Seebeck coefficient, trap density, quantum efficiency, work function, effective mass of electrons and holes, and/or the like.
  • the models herein may be prediction models configured to predict data associated with one or a plurality of materials, based on second data associated with the one or plurality of materials.
  • the model may be configured to generate a material property prediction based on a material identifier. Additionally, or alternatively, the material identifier may include composition and structure data.
  • the material property may be a contextual material property
  • the material identifier may be composition data, structure data, and contextual conditions data.
  • the model may be configured to generate a simulation of the material based on a material identifier. Additionally, or alternatively, the model may generate the simulation based on a material identifier and a set of input contextual conditions. Additionally, or alternatively, the simulation may be a spatio-temporal simulation. Additionally, or alternatively, the spatio-temporal simulation may show the evolution of a material in its desired use case environment. Additionally, or alternatively, this may be a simulation of a building material of a particular geometry bearing a compressive load for an amount of time, at a particular temperature and pressure condition.
  • the simulation may output a simulation of a material at a particular scale, wherein the scale may be the atomic scale, unit cell scale, nanoscale, microscale, macroscale, and any scale in between. Additionally, or alternatively, the simulation may be a multiscale simulation, where a simulation of the material is generated at multiple scales simultaneously. Additionally, or alternatively, the simulation may be a DFT-based simulation or a molecular dynamics (MD) model. Additionally, or alternatively, the simulation may be a simulation of a chemical reaction. Additionally, or alternatively, the simulation may include timeseries data or other data, associated with the material composition, structure, and properties of the material. Additionally, or alternatively, simulations may be constructed using a generative model.
  • MD molecular dynamics
  • simulations may be constructed using physico-chemical models. Additionally, or alternatively, simulations may be constructed using a hybrid machine learning, physico-chemical model, such as a hybrid machine learning, DFT model.
  • the material identifier may include a material property. Additionally, or alternatively, the simulation may display the evolution of this property over time.
  • properties may be predicted using compositional and structural data, followed by a simulation. [00549] In other embodiments, the model may be configured to predict future properties of a material based on live and/or historical properties data of that material.
  • live and/or historical properties data may be collected from sensor devices, digital records (e.g., materials compliance testing), human input, databases, and/or the like.
  • the model may be configured to predict first compositional properties using second compositional properties.
  • the input into the model herein may be a prompt.
  • the model may extract relevant data from the prompt.
  • the model may convert the relevant data into an internal vector space representation.
  • the model may tokenize the prompt through a transformer architecture.
  • the tokens may be embedded in a vector space.
  • the token vector space may be mapped to an internal vector or complex vector space representation of material space.
  • the vector in material space may be indicative of one or a plurality of material identifiers, or one or a plurality of material descriptors.
  • property prediction and simulation may be undertaken using any models including, but not limited to, linear models and regression models, decision trees and ensemble models, support vector machines, neural networks, gaussian process regression, physico-chemical models, hybrid models, and/or the like.
  • the model may be trained on material data which forms a training dataset. Additionally, or alternatively, the training dataset may include material identifiers, composition data, structural data, property data, and contextual conditions data associated with the material or plurality of materials under consideration.
  • composition data may include any and any combination of the embodiments for composition data, structure data, and/ or property data described herein.
  • the training dataset may be labeled or unlabeled.
  • the training dataset may include data in different representations, formats, and from different data sources including but not limited to the representations (e.g. Voxel, Graph-based, and/or the like) and formats (pdf, png, csv, and/or the like), and data sources listed elsewhere herein.
  • the training method may comprise splitting the training dataset into training data and testing data. Additionally, or alternatively, the training data is split into inputs, and outputs.
  • the material under consideration may be a crystal
  • the model may be prediction models configured to predict data associated with a crystal material, based on second data associated with a crystal material. All the model methods described herein throughout this disclosure for models may be applicable for crystal models.
  • the model may be trained on crystal material data.
  • models directed to predicting the properties of a material in target contextual condition, predicting compositional data, simulating one or more units of material over time, predicting composition and/or properties of a material that goes through a production process based on the production process & the raw materials, predicting attributes of production processes, based on representation of process, and/or any other predictive model described herein may be used to predict the properties of one of or a plurality of crystals and/or any other material described herein.
  • Prediction Crystal Prediction Embodiments [00557]
  • the methods and/or models herein may be used to predict the properties of one or a plurality of crystals. Additionally, or alternatively, the predicted properties may be associated with a set of contextual conditions for the crystal.
  • the method may include the use of a machine learning model.
  • the machine learning model may be a neural network.
  • the neural network may be a graph neural network.
  • the model method may include the use of a physico-chemical model.
  • the physico-chemical model may be a DFT based model or Molecular Dynamics model.
  • the prediction methods and/or models herein may be based on a crystal material identifier.
  • the crystal material identifier may include crystal composition data and/or structure data.
  • the input to the model may be a crystal descriptor material identifier based on a number of composition data and/or structure data.
  • the composition data and/or structure data may include one or any combination of the following: unit cell data; species of the atom; atomic number; atomic radius; valence electron data; electronegativity; valence electron configuration; atomic mass; bond type e.g.
  • the predicted properties may include one or any combination of the following data: Bandgap Energy; Carrier Mobility; Thermal Conductivity; Dielectric Constant; Recombination Rate; Electrical Conductivity; Breakdown Voltage; Thermal Expansion Coefficient; Electron Affinity; Optical Absorption Coefficient; Refractive Index; Saturation Velocity; Seebeck Coefficient; Trap Density; Quantum Efficiency; Work Function; Effective Mass of Electrons and Holes. Additionally, or alternatively, the properties may be predicted with associated contextual conditions including but not limited to: temperature; electric field intensity; magnetic field intensity; radiation exposure; stress and/or strain; humidity and/or moisture; external circuit parasitic interactions; biasing conditions.
  • the material identifier may include sensor data from a high frequency E&M wave-based sensor device. Additionally, or alternatively, the composition data and structure data may be derived from sensor data from a high frequency E&M wave-based sensor device. Additionally, or alternatively, the high frequency E&M wave-based sensor device may be any one of: FTIR device; LIBS device; Raman device and any other spectroscopy based, microscopy based, and imaging based device (e.g., hyperspectral imaging device). Additionally, or alternatively, the prediction method may be in whole or in part based on the symmetries of the crystal structure.
  • a crystal property prediction method may include ingesting a first crystal material identifier, generating a second crystal material identifier based on the first, and outputting the second crystal material identifier.
  • the first crystal material identifier may include crystal composition and/or crystal structure.
  • the second crystal material identifier may include a crystal property.
  • the second crystal material identifier may include third data indicative of a convex hull.
  • a crystal simulation method may include ingesting a material identifier, generating a simulation based on the material identifier, outputting the simulation.
  • the simulation may be a spatio-temporal simulation.
  • the simulation may include charge carriers traveling through a crystalline structure, where the crystalline structure may optionally be a semiconductor.
  • the simulation may include data indicative of conductivity and/or electron and/or hole mobility.
  • the simulation may include structural and/or compositional data for the crystal unit cell and/or supercell.
  • the second metal alloy material identifier may include a metal alloy property.
  • the metal alloy composition and/or structure data may include one or any combination of the following: proportion of raw materials (e.g., by weight, volume, atomic quantity, and/or molar ratios); base metal; alloying elements; phase composition (e.g., solid solutions, intermetallic compounds, and/or mixed phase); microstructure (e.g., grain structure, and/or distribution of phases); crystallographic structure (as outlined elsewhere herein if alloy is crystalline); atomic structure; hume-rothery rules compliance; lattice structure; grain distribution; defect density; precipitate and/or inclusion data; allotropic transitions; alloy type (e.g., stainless steel); probability distributions (e.g., describing elemental composition, grain type distribution, grain size distribution, grain orientation distribution, defect distribution, phase distributions, and/or the like); valence electron concentration.
  • the metal alloy property data may include any one or plurality of the following: Tensile Strength; Yield Strength; Hardness; Ductility; Elastic Modulus; Fatigue Strength; Fracture Toughness; Impact Toughness; Melting Point; Thermal Conductivity; Coefficient of Thermal Expansion; Creep Resistance; Corrosion Resistance; Oxidation Resistance; Electrical Conductivity; Electrical Resistivity; Magnetic Permeability; Wear Resistance; Friction Coefficient; Density; Formability; Castability; Machinability; Weldability; Surface Roughness. Additionally, or alternatively, the second metal alloy identifier may be predicted using interpolation based on the properties of other metal alloys.
  • the attributes and/or properties herein may be predicted and/or determined, in whole or in part, based on the data generated by any quantum sensor herein.
  • the material may be a metal alloy used in any of the methods herein.
  • the metal alloy may be any one of: Steel; Stainless Steel; Brass; Bronze; Inconel; Nichrome; Titanium Alloy; Aluminum Alloy; Duralumin; Monel; Hastelloy; Nitinol; Cupronickel; Pewter; Solder; Zinc Alloy (Zamak); Superalloy; Magnesium Alloy; Cobalt-Chrome Alloy; Lead Alloy; Gold Alloy; Silver Alloy; Beryllium Copper; Tin Alloy.
  • a method for battery chemistry simulation may include receiving as input a first battery chemistry material, as well as a battery system structure representation, generating a simulation of the battery creating electrical energy (e.g., current), and/or outputting the simulation. Additionally, or alternatively, the simulation may be a spatio- temporal simulation. Additionally, or alternatively, the simulation method may further include receiving as input a set of contextual conditions, generating the simulation within the set contextual conditions, and/or outputting the simulation within the set contextual conditions.
  • an amorphous material property prediction method may include receiving as input a first amorphous material identifier, predicting an amorphous material identifier based on the first, and/or outputting the second amorphous material identifier. Additionally, or alternatively, the first amorphous material identifier may include an amorphous material composition data and/or structure data. Additionally, or alternatively, the second amorphous material identifier may include an amorphous material property. Optimization Embodiments [00567] Any of the models, methods, and/or processes described herein may be applicable to modeling any of the materials described here.
  • any of the generative models described herein may be configured to generate material-related data of one of or a plurality of crystals.
  • models directed to generating one or a plurality of material compositions based on target objectives, generating a plurality of material compositions continuously (e.g., discovery mode), and/or generating one or a plurality of material production processes based on a desired material output and/or any other generative model described herein may be used to generate properties of one of or a plurality of crystals and/or any other material described herein
  • Generation Crystal Embodiments may include receiving as input a first crystal material identifier, generating second crystal material identifier, and outputting the second crystal material identifier.
  • the first crystal material identifier may include data indicative of a target objective.
  • the second material identifier may include data indicative of material composition and/or material structure.
  • the target objective may include any attribute for the crystal material.
  • a data object may be a material identifier that may include a material ID, a material name, and/or a material type.
  • a C40 mix may be defined as a concrete mix that may reach a minimum of 40 MPa of compressive strength by 28 days, if cured as a standard cube (or cylinder) in standard conditions (in a temperature- controlled water bath at a fixed temperature).
  • a C60 has an identical definition but must reach a minimum of 60 MPa instead. Therefore, material identifiers (e.g., names) hold information regarding the particular mixes the models described herein are operating upon.
  • a non- exhaustive list of potential material structures considered by the present disclosure may include unit cells and/or supercells, lattice parameters, symmetries, multi-scale structure (e.g., atomic structure, nanostructure, microstructure, macrostructure, and/or the like), crystallinities, grain sizes and distributions, porosity and pore size distributions, descriptors and/or fingerprints (e.g., bond length, atom locations (e.g., vectors), and/or the like), and/or the like.
  • a material identifier may include material properties.
  • Objective functions of the present disclosure may be directed to and/or may include L1 regularization, L2 regularization, parameters (e.g., model attributes that change during optimization or learning), hyperparameters, weights, biases, regularization parameters, constraint parameters, multi-objective functions (e.g., weights (that determine importance of each objective terms), pareto-based, evolutionary algorithm methods, and/or the like), and/or the like.
  • objectives can be encoded into utility functions.
  • Utility functions may be similar to objective functions but may be used in cases where a user’s preferences may be defined over multiple possible outcomes into one function.
  • a non- exhaustive list of utility functions considered by the present disclosure may include cumulative reward function, expected utility, payoff function, linear utility, multiplicative utility, exponential utility, logarithmic utility, weighted sum utility, risk-aware utility (to penalize risk or uncertainty in material behavior), piecewise utility, pareto utility, and/or the like.
  • Potential objective considered by the embodiments of the present disclosure may include minimize cost, maximize performance, minimize embodied carbon, maximize or minimize any property (optionally up to a threshold), maximize strength-to-weight ratio, minimize material cost, maximize durability, maximize recyclability, minimize environmental impact, maximize fatigue resistance, maximize thermal conductivity, maximize corrosion resistance, minimize weight, maximize impact resistance, minimize manufacturing complexity, minimize production time, maximize availability of raw materials, maximize energy efficiency in production, maximize ease of processing, minimize maintenance requirements, maximize lifecycle sustainability, minimize embodied carbon in material, minimize carbon footprint, minimize waste generation during production, maximize use of renewable materials, minimize water usage during production, maximize potential for reuse or repurposing, maximize resistance to wear and tear, maximize UV resistance, maximize fire resistance, maximize electrical conductivity, minimize electrical resistivity, maximize dielectric strength, maximize optical clarity, maximize light absorption for energy applications, maximize electromagnetic shielding effectiveness, minimize electromagnetic interference, maximize chemical resistance, minimize material degradation due to chemical exposure, maximize ease of machining and forming, maximize compatibility with additive manufacturing, maximize resistance to weathering, maximize surface finish quality,
  • a non-exhaustive list of potential material spaces considered by the present disclosure may include N-dimensional space of compositions (e.g., raw material proportions, chemical composition (e.g., a material space of each combination of elements in periodic table), any other way of representing composition, and/or the like), N-dimensional space of material composition and structure (e.g., similar to the N-dimensional space of compositions, but space encodes information about the structure as well), N-dimensional space of properties, N-dimensional space of sensor signatures, N-dimensional space of compositional arrangements and/or production processes (e.g., where each “step” in the production process may be represented as a vector or matrix that operates of “material states”), and/or the like.
  • N-dimensional space of compositions e.g., raw material proportions, chemical composition (e.g., a material space of each combination of elements in periodic table), any other way of representing composition, and/or the like
  • N-dimensional space of material composition and structure e.g., similar to the N-dimensional space of compositions
  • processes may be represented as operators, or matrices or tensors that may act on material vectors and modify or evolve such vectors such that they move in material space.
  • matrices may be represented in basis of the vector space. For example, the vectors may become entirely new vectors if the process evolves the material into a completely new state (e.g., chemical reaction). Additionally, or alternatively, a process which doesn’t fundamentally change the nature of the material might just scale it (e.g., cutting a piece of material in half, could make the vector half the length).
  • the step of comparing the desired output with the executed output may be modified such that a derived quantity of the output is calculated and minimized (or maximized) depending upon the nature of the unsupervised search.
  • the procedure described above may sometimes be executed in one iteration, or sometimes in many.
  • the procedure may sometimes be executed analytically, and sometimes may be executed numerically. All of these are processes that may be used by any of the models described herein, depending upon the circumstance of the data and the model being trained.
  • reinforcement learning with human feedback may be a training procedure used to train any of the models described herein.
  • RLHF is a means of modifying the standard reinforcement learning approach to include human feedback into the iterative optimization procedure.
  • Embodiments of the present disclosure may include a plurality of types of optimization algorithms (e.g., similar to the objective functions described herein).
  • Embodiments of the present disclosure may include a plurality of input features.
  • a large language model may be configured to interact with a user.
  • An example interaction may be the user inputs a prompt and the LLM ingests the prompt.
  • the use of probabilistic models over a set of point values may allow for conversion of any point value into one or more range values associated with that model output.
  • the point value for a predicted temperature may be 24.3C, but the probabilistic uncertainty on that output may provide the range of values (23.8C, 25.2C), where the first and last temperature in the range denotes the lower and upper bound on the uncertainty to within a 99.9% confidence level, respectively.
  • the present disclosure provides for the explainability of the models’ predictions, evaluations, recommendations, and/or generations.
  • a combination of models, described above are trained to solve for the problems outlined by some of and/or all of the model descriptions herein.
  • a combination of probabilistic inference on uncertainty, and probabilistic or causal graph algorithms on the space of all inputs/outputs from the models herein may be used to trace the causal and logical reasons for model predictions, evaluations, recommendations and/or generations.
  • the methods described herein do not just provide a set of output values and probabilistic uncertainty on those values, but also provide a model-based reason for those outputs and probabilities.
  • automated explanatory outputs may range from physical/chemical insights (e.g., wherever physical/chemical models are utilized) to internal “representational” understandings, that derive from the internal component dynamics of the computation models that have been numerically derived from the data and the training procedure/s.
  • the models may be continually updated, where updating is done as a continuous process of training as new data arrives from any set of information sources.
  • Normalization and Self-Detection [00616] A given material will behave differently in different contextual conditions - its contextual material properties may be different (e.g. Strength, Durability, Shrinkage, Strain, Workability, will evolve differently in environments with different temperatures). Therefore, in order to understand the material properties of different materials, it is vital to account for differences in contextual conditions.
  • One way to overcome this challenge is therefore to create a “baseline” across which materials within different contextual conditions will be comparable. example, a material poured into a cube of sides 10cm, may cure very differently when compared to the same material poured into a cubical slab of sides 3m.
  • Another approach may include using physico- chemical models which understand heat diffusion in physical systems.
  • the above may be summarized as measurement devices produce measurements about materials which may later be used in the models herein. These measurements may be adjusted (or corrected by using self-detection/sensor context awareness data. Therefore, data collected in different contextual conditions become comparable. In doing so, the models may allow for not only the collected data but also the materials themselves in different contextual conditions to be made comparable through this adjustment/normalization.
  • Spatially and Temporally Normalized Models [00619] In general, a contextual material property may be defined at a certain time and at a certain location within the instantiated use-case of the material in question.
  • the models herein may account for the contextual material property as a “bulk” value (independent of spatial position) and/or a “constant” value (independent of time).
  • a “bulk” value independent of spatial position
  • a “constant” value independent of time
  • normalization is used to define the ability of a model to account for the functional variation in spatial and/or temporal location; where that model is able to modify the contextual material properties in its outputs based on information about the location and/or time of contextual conditions.
  • the model may receive the temperature time-series from one temperature sensor, its radial distance, the geometry of the cementitious material and the geographic location, and output a mesh of temperature time- series, equi-spaced throughout the 3D geometry of the material.
  • This example model may be trained on a hybrid model that utilizes functions that may be inspired by physical thermal models, artificial neural networks (trained on data of the four types described above, and for a variety of different cementitious material recipes and geometries) and Bayesian inference. That model may then be embedded inside a “material identifying” model, that will use the predictions of the contextual material properties (in conjunction with an empirically solved physical model) to obtain an assessment of which material is being used.
  • the physical model module utilizes the physical principle (based on physical assumptions) that the heat within a material dissipates as the inverse exponential of its distance from the heat source.
  • the weights are solved by training against the sensor data time-series values for all positions, and across all measured instantiation of materials (where each material is identified by its compositional material properties).
  • the weights may be trained under the assumption that they are constants at a given time and for a given material composition (in general, the temperature of the surface of the material will vary with time as the ambient temperature varies).
  • a set of weights may be obtained on a per-material basis, that characterizes the thermal properties of a given cementitious material composition.
  • a principal mode may be the planning mode (e.g., ‘static’ mode). This mode optimizes materials prior to the project having begun and prior to having generated any sensor data. Some features of this mode may include material properties may be predicted, materials may be evaluated against performance requirements or target contextual material properties, materials may be recommended based on a ranking derived from a utility function (e.g., lowest carbon mix), and material compositional arrangements may be adjusted or generated in line with performance requirements or target contextual material properties and a utility function or ranking.
  • These models may be continuously learning and improving from the execution phase (as more data is gathered, predictive power increases).
  • a use case may be the static case that optimizes based on predictions that may be based on statistical performance of particular properties (ideally with known standard deviations / confidences) or physico- chemical knowledge of the principles which underpins them. These predictions may be based on historical measurements (e.g., whom the supplier of a material is, but not its reactivity in that particular batch, however, these predictions may lack data regarding the specific project the models may be optimizing for).
  • models of the present disclosure may include a materials screening feature.
  • models may use hard constraints to screen and discard materials from the get-go such as in evaluation or generation/discovery models.
  • material screening may reduce the evaluation and/or the search space.
  • the requirements may include standard material properties (such as strength, durability, or stiffness) and in-use performance criteria (such as achieving specific performance levels within a set time frame or environmental condition), which may be predicted using one or more of the models described herein.
  • the system may also be capable of generating new material compositional arrangements based on the parameters provided by the user. These newly generated formulations are evaluated and included in the pool of candidate options, where appropriate.
  • Candidate formulations may be ranked based on a customizable utility function.
  • each model may even be able to orchestrate a sequence and/or parallel combination of use of each model (e.g., use MO-1 & MO-2 in parallel, feed the output data to another MO-X).
  • This system may allow the method to act as a distributed system of models that may fulfil different purposes and may be used multiple times in parallel or in sequence.
  • Going one step further, for each one of the models described herein it may be possible to have different versions of the model, which may be trained in different ways to create intentional biases and/or specialize the models towards outperforming in a particular field.
  • one version of a model MO-X may be trained on a large portion of incomplete data (which may allow the model to become robust towards incomplete data) whereas another may be trained on predominantly complete data, which may be more accurate than the models trained on partial data and may be used whenever additional data is available.
  • Another example may be a model trained predominantly on mixes composed of SCMs (supplementary cementing materials), and may therefore be particularly effective at predicting, evaluating, and/or generating SCM-based material designs.
  • This latter example may be generalized as training models to effectively work in different volumes or parts of material space (e.g., model for C80 and above, model for materials below C50, and/or the like). In some embodiments, this may be described as an adaptive model.
  • One instantiation of the distributed model system may be a distributed system of GANs (generative adversarial network), where a system of distributed GANs, configured with different biases, may act as an ensemble which may be intentionally skewed towards certain types of mixes over others.
  • the models herein may be trained via data generated from simulations. These simulations may be chemical simulations, in some embodiments.
  • material simulations may be executed on a quantum processor, and/or using a quantum algorithm, and the output of those simulations may be used to train any of the models described herein, or as an input to any of the models described herein. Additionally, or alternatively, simulations and/or predictions of the models herein may be done on quantum processors and/or using quantum algorithms.
  • Embodiments of the present disclosure may be configured to simulate material and generate material predictions.
  • the models considered by the present disclosure may predict properties of a material in target contextual conditions.
  • the models described herein may generate predictions based on material compositional data.
  • the models described herein may generate predictions based on property data of another type (or partial property data).
  • the models described herein may generate predictions based on historical properties or compositional data of the same material or anther material.
  • the models herein configured as a prediction model may generate predictions based on evolutions of material properties over time and/or space for given contextual conditions.
  • the models considered by the present disclosure may be configured to predict material compositional data.
  • the models may generate predictions based on compositional data of another type (e.g., structure from chemical formulation).
  • the models may generate predictions based on property data (e.g., to identify material or instantiation of material).
  • the models may generate predictions based on prior compositional data or property data.
  • the models may generate predictions based on evolutions of compositional data over time and/or space.
  • the models considered by the present disclosure may be configured to simulate one or more units of material over time. In such embodiments, the models may simulate material in a set of contextual conditions (based on contextual conditions).
  • the models may simulate chemical reactions or interactions.
  • the models may predict the composition or properties of the final output of the reaction, based on the input raw materials and the one or more contextual conditions during the reaction.
  • chemical reaction may be atomic, molecular, or other basic unit (e.g., grains, composites, and/or the like).
  • the simulation may include material composition, structure, and/or properties. Additionally, or alternatively, units of materials may be at any scale (e.g., atomic ⁇ macro), and/or the simulations may be any spatio-temporal (4D) simulation, or just a spatial (3D) simulation.
  • the material simulation model may include the prediction capabilities other models described herein.
  • the models considered by the present disclosure may be configured to predict composition and/or properties of a material that undergoes a production process based on the production process and the raw materials described herein. In such embodiments, control parameters of processes and/or process contextual conditions may be input into the model for prediction. In some embodiments, the models may be configured to predict properties of a known material. In some embodiments, the models may be configured to predict compositions of output materials. Additionally, or alternatively, the models may generate predictions based on sensor data monitoring the production process. [00643] In some embodiments, the models considered by the present disclosure may be configured to predict attributes of production processes, based on representations of processes.
  • a model may predict, determine, and/or estimate attributes of production processes (e.g., cost, carbon, time efficiency, and/or the like) based on a representation of the production process, which may include material data about raw materials, intermediate material, output materials, the production pathway and processes themselves, control parameters, and/or equipment used.
  • attributes of production processes e.g., cost, carbon, time efficiency, and/or the like
  • a representation of the production process which may include material data about raw materials, intermediate material, output materials, the production pathway and processes themselves, control parameters, and/or equipment used.
  • any of the models described herein directed to predicting properties of a material in target contextual conditions may follow formal input and output descriptions such as X (+ Contextual conditions optionally) ⁇ Properties (+ Contextual conditions optionally).
  • X Compositional data
  • the formal descriptions may take the forms of Compositional data ⁇ contextual material property, Compositional data ⁇ static material property, and/or Compositional data ⁇ contextual + static material property.
  • X Property such that the model may use past properties to predict future properties.
  • any of the models described herein directed to predicting compositional data the models may follow a formal input and output descriptions such as X ⁇ Compositional Data.
  • the models described herein may optimize both a material and a material’s production process based on target properties and target use cases /or target behavior within an environment and/or contextual conditions (e.g., may use NSGA-I, NSGA-II, NSGA-III algorithm ⁇ non-dominated sorting genetic algorithm I, II & III).
  • the models described herein may optimize both materials and production processes for carbon and/or sustainability.
  • the models herein may use spatio-temporal simulations (e.g., digital twin) to create virtual environments where material may be virtually simulated and/or tested against desired properties and/or use case.
  • the models herein may generate materials for a defined target use, and/or discover all possible materials in a given space (e.g., constrained by available inputs).
  • Embodiments of the present disclosure may use sensors for training and/or data input. Further, the sensors may include any of the sensor devices as described herein.
  • embodiments of the present disclosure may execute discovery on quantum computers and/or use quantum simulations. Further, any of the sensor devices described herein may leverage quantum sensing.
  • the models herein may be configured to generate chemical formulas (e.g., using stable diffusion model or other GAN, based on the rules of chemistry, and/or the like).
  • the models herein may incorporate “Meta-properties” to generate materials accounting for cost and/or carbon.
  • a first method may be using a GPT/LLM to estimate monetary cost and embodied carbon for producing said material.
  • a second method may be training a model on cost and carbon data for a wide variety of materials and allowing the model to interpolate/infer for the new material based on the old material.
  • the model may create a mapping and/or function between material space and cost/carbon space. Further, embodiments of the present disclosure may methods described herein to make the function ‘continuous,’ thus allowing for interpolation for candidate materials (e.g., materials not seen previously in material space).
  • Embodiments of the present disclosure may include multiple specialist models (e.g., for different parts of prediction, simulation, optimization, and/or the like) and/or model orchestration.
  • Material Production [00713] Material productions considered in the present disclosure may be configured to employ any of or any combination of data object types as described herein.
  • a non-exhaustive list of data objects that may be used in any material production herein may include materials (e.g., raw materials, intermediate materials, output materials, final product, and/or the like), processes (e.g., may be defined as anything that makes a material evolve from state ⁇ to state ⁇ + ⁇ ), production processes (e.g., may be defined as a process with the goal of producing a final product or target material), equipment (e.g., machinery and/or apparatuses which may execute or may be used to execute a process), control parameters (e.g., controllable external parameters of a process), and/or the like.
  • material productions considered in the present disclosure may be configured to employ any of or any combination of models as described herein.
  • a non-exhaustive list of models that may be used in any material production herein may include a model configured to predict composition and/or properties of a material that goes through a production process based on the production process and the raw materials (e.g., Raw Materials + Process ⁇ Output Materials), a model configured to evaluate a set of production processes (or aspects of a production process) against a set of target objectives, and output an evaluation identifier (e.g., Process + Evaluation Parameter ⁇ Process Evaluation and/or Processes + Target Objectives ⁇ Processes Ranking), a model configured to optimize one of or a plurality of production processes based on target objectives and material output (e.g., Process + Target Objective ⁇ Optimized Process), a model configured to generate one of or a plurality of material production processes based on a desired material output (e.g., Desired Material Output ⁇ Process), and/or the like.
  • a model configured to predict composition and/or properties of a material that goes through a production process based on the production process
  • a production pathway may include the creation of a metal alloy from its constituent metallic elements, the chemical synthesis of a polymer, the construction of a crystalline material from its constituent parts, the conversion of a pure element into the same pure element configured with another structure, the mixing of raw materials to form a composite material, the construction of a battery from its raw constituents, etc.
  • the term “Production Process” and/or “Compositional Arrangement” may be used to refer to any step, intermediate step, or plurality of steps that form part of a Production Pathway, in whole or in Part, to evolve one or a plurality of materials from state S to state S + dS or S + ⁇ S.
  • a production process may include the smelting of metal ores, in the wider process of creating a metal alloy from its constituent metal ores.
  • the term “Raw Materials” may be used to refer to any material, in the context of a production process or production pathway or compositional arrangements, which is an input to the production process or pathway.
  • cement is an intermediate material in the production process for the creation of concrete, from quarried material (e.g. from limestone).
  • the term “Production Equipment”, may be used to refer to any apparatus or machinery which executes or is used to execute a production process, in whole or in part.
  • production equipment may refer to one or a plurality of any of the following: CNC cutter, extruders, furnace, kiln, grinding machine, batching machine, mixer, blenders, injection molding machine, cooling systems, chemical reactors, crystallizers, filters/separators, and more.
  • Process Parameter may be used interchangeably to refer to any parameters or variables that may be controlled during a production process or pathway and that may influence the production process or its output. For example, this may refer to temperature, pressure, time/duration, flow rate, pH, raw material proportions, intermediate material proportions, agitation/mixing speed, heat/convection transfer, E&M fields, and/or the like present during the process and more.
  • Production pathway attributes may, in some embodiments, be features predicted by the models described herein, or may be included in the representation or description of the pathway.
  • a non-exhaustive list of examples of production processes or pathways considered by the present disclosure may include polymerization, mixing, extrusion, cooling, cutting/finishing, smelting, refining, casting, hot rolling/cold rolling, melting, crystal growth (Czochralski process), doping, cutting/wafering, polishing and etching, precursor gas preparation, chemical vapor deposition (CVD), plasma-enhanced CVD (PECVD), hydrogenation, hydration, annealing, fiber preparation, and/or the like.
  • CVD chemical vapor deposition
  • PECVD plasma-enhanced CVD
  • a non-exhaustive list of examples of equipment used in production processes or pathways considered by the present disclosure may include reactor vessel, mixer, heat exchanger, centrifuge, conveyor belt, furnace, distillation column, filter press, spray dryer, extruder, homogenizer, ball mill, kiln, autoclave, crystallizer, evaporator, pulverizer, conveyor dryer, rolling mill, sieve, granulator, magnetic separator, paddle dryer, rotary dryer, agitator, fluidized bed dryer, clarifier, electrostatic precipitator, shredder, vibrating screen, injection molding machine, baler, arc furnace, induction furnace, and/or the like.
  • the models herein may be configured to evaluate a set of production processes (or aspect of a production process) against a set of target objectives and output an evaluation identifier.
  • the models may evaluate pathways in real time based on sensor data including quantum sensing data and contextual condition data.
  • a feedback loop may allow the system to switch pathways if the current chosen pathway does not have the optimal evaluation anymore (e.g., dynamic pathway evaluation).
  • an objective function may have dynamic weights for target objectives (e.g., cost, carbon, time, or the like)
  • a scenario simulation engine for pathways may be used to enable simulating changes to process.
  • an “AI Engine” may refer to an Artificial Intelligence Engine (made up of one or more models and optionally an orchestration layer) that parses the data from the Data Engine and performs the inference required to deliver an absolute material identifier mode and a perturbative material identifier mode.
  • the goal of the system is to determine a material identifier and to (optionally) classify the material.
  • the absolute identifier determination is made on the basis of measurements of the material & contextual conditions (for the material and sensor devices used).
  • the models can generate a representation of material -space, and a classification system.
  • the types of data used may include, but are not limited to, measurements of temperature, ultrasonics, and impedance (including mechanical, electrical, electrochemical, wave- impedance, acoustic, elastic, and magnetic impedance), along with conductivity, pH levels, and various forms of spectroscopy and imaging (including hyperspectral imaging).
  • the device data measurement types as detailed herein, are applicable here, encompassing outputs from wave- based sensors and/or any quantum sensing technique disclosed herein.
  • data related to the material s position, orientation, geometry, and formwork. This may include employing context awareness models, methods, and devices to understand sensor contextual conditions (pertaining to the sensor itself) and material contextual conditions (related to the local material volume under observation).
  • the material identification engine and the material perturbation detector may be configured to recognize patterns within multivariate data and map these patterns onto material properties that define a material. This process facilitates the determination of the identifier for the likely material.
  • the operation commonly referred to as ‘material identifier’ may encompass this methodology.
  • Material identifier may encompass a variety of methodologies. For instance, one approach may involve using sensor measurements (e.g., strain gauges, viscosity, acoustic, resistivity, temperature sensors) to report on the physical properties such as fineness, strength, durability, and workability.
  • material identifier by composition, each illustrating a different basis or representation of composition.
  • material identifier can also be performed in property representation, arbitrary identifier (name) representation space, or an entirely different representation space.
  • the Generative Model may be employed at any stage of this process to augment incomplete or low-quality datasets, thereby enhancing the performance and output of the AI Engine models. Following each successful identification, there may be the possibility of updating or retraining the AI Engine to maintain or improve its efficacy.
  • Example System Details, Inputs and Outputs Example Inputs [00762]
  • concrete being a composite material comprising various constituents, presents complexities in modeling or predicting its behavior.
  • the environment of a concrete pour notably the construction site, constitutes a complex and variable system. Consequently, numerous contextual conditions and diverse types of measured data must be processed by the AI engine (e.g., the processor described herein). This processing is essential for accurately parsing and understanding the identifiable aspects of any given concrete material design.
  • the AI engine e.g., the processor described herein. This processing is essential for accurately parsing and understanding the identifiable aspects of any given concrete material design.
  • the AI engine s ability to effectively navigate and interpret this multifaceted data landscape is used for reliable and precise identification and analysis of concrete material properties in varying environmental and compositional scenarios.
  • the input data for the AI engine may be classified into two primary categories, contextual condition data and measurements.
  • Other inputs to the system may include a variety of data types, including historical data, expectations and requirements, identification representation parameters, classification system parameters, and others, such as outputs from other models.
  • Historical data may be categorized into priors or prior knowledge about the material under consideration, and knowledge about previously identified materials.
  • Expectations and requirements may be useful inputs, especially in perturbative cases. They relate to the anticipated properties of a target material in a given contextual condition. These expectations can cover a range of aspects, including compositional properties, material properties (both static and contextual), identifiers, sensor signals, and contextual conditions, or they may pertain to an arbitrary representation of material space. Requirements, akin to expectations, may be derived from specifications and other documents.
  • the system may include a data engine (for data aggregation and preparation), and an AI engine.
  • the AI Engine may further be divided into two distinct modes: (1) absolute material identifier and (2) perturbative material identifier. Each mode may incorporate four specialized models. Additionally, an orchestration system may be used to facilitate interaction between these models, and a generative model may be used to address data gaps.
  • the models may include (i) the representation generator; (ii) the classification system generator; (iii) the material identification engine; and (iv) the material classification engine.
  • the sensor data may be represented as a multivariate N-dimensional vector space of time-series data. Each vector in this space, potentially sampled at varying rates, may correspond to a different type of data (like temperature or ultrasonics) taken at each physical location.
  • the system may handle a multitude of these N-vectors, with M representing the number of spatial locations sampled, either within or outside a concrete pour.
  • This may include the handling of spatio-temporal data (such as from spectroscopy, imaging, or infra-red sensors), impedance data (which are complex numbers), and other irregular data values. These data may be sampled and formatted in a way that the system, using attendant physics models, can interpret to represent conditions.
  • the vector spaces, or signals are generally not orthogonal, and any correlation between signals may be managed through dimensionality reduction techniques.
  • the signals may undergo transformations as necessary, including normalization, standardization, Fourier transforms, and/or the like. Scalar features may then be extracted and sent to the data parser for interpretation through the internal schema.
  • An input vector, X is subsequently composed.
  • This vector may combine the mapped vector of scalar data and the transformed time-series signal vector, readying it to be input into the AI Engine or its constituent models.
  • Example AI Engine Models and Outputs [00776] In the Absolute Case of the AI engine, by internalizing multivariate data sources, a virtually complete model of materials may be constructed. This model may be defined through multiple representations, including a model based on providing a Material ID, a model based on the Chemical & Material composition of the material, a model based on the Physical Properties of the material , and other arbitrary identification bases. The configurations of this model may be versatile, designed to accommodate varied and even incomplete data.
  • the primary model may be a supervised learning model, which requires labeled information for its operation.
  • physico-chemistry based equations may be incorporated into these models.
  • This approach integrates the fundamental principles of physico-chemical interactions within the learning process.
  • unsupervised learning techniques may be employed. These techniques are instrumental in identifying patterns and correlations within the data without the need for labeled datasets, thus enhancing the model’s ability to handle diverse and complex data structures.
  • generative model builder tools may be developed for creating mappings between contextual conditions and material properties to their respective material compositions, formulations, and designs. The generative model may be used in instances where there is missing information, invoked to fill these gaps, thereby ensuring the continuity and comprehensiveness of the model.
  • the functionality may be distributed across four distinct models, Representation Generator, Classification System Generator, Material Identification Engine, and (Material Classification Engine, each serving a specific purpose in the process of material identification and classification.
  • the Representation Generator may be tasked with generating a desired representation for material -space. The representation could be based on various criteria such as compressive strength, workability, shrinkage measures, or material formulation.
  • the Classification System Generator may generate a classification system over this chosen representation. This involves segmenting the volume of the material - space representation into sub-volumes, each representing a specific class or family within the material space.
  • Material Identification Engine may be responsible for identifying the material within the given representation.
  • This identification can be based on various aspects such as material properties, material composition, name or arbitrary identifier, sensor signals, or an entirely different representation which may have been generated by the model itself.
  • An additional step in this process may involve further processing or combining these representations to generate a unique fingerprint and rebasing of material -space into fingerprint space.
  • Material Classification Engine may classify the identified material within the established classification system and representation.
  • the representations may include identification by name (arbitrary identifier), identification by compositional properties (what is it?), and identification by properties (how does it behave?).
  • identification by name the identifier or fingerprint of the material may be defined as an “ID” - an artificial nametag generated by the engine.
  • the AI Engine can generate the output identification of the material by matching to existing database classification, augmenting existing database classifications, or generating a new database classifications.
  • matching to existing database classifications may involve comparing the output fingerprint of the material to a database of concrete materials. This database could be based on an existing standard or relate to previous materials identified by the AI engine.
  • the matching process can be executed through supervised learning techniques.
  • the AI Engine may be trained using labeled data that matches the categories or classifications in the existing database. Through this training process, the AI engine may “learn” the distinctive material properties of each material in the database over numerous iterations.
  • generating a new database classification may include classifying the output fingerprint into an entirely new classification, one that is fully generated by the AI engine.
  • the AI engine may use advanced techniques such as dimensionality reduction and meta-regression models, or the initial layers of a Convolutional Neural Network (CNN). These techniques form the core of the engine’s internal classifier.
  • CNN Convolutional Neural Network
  • the AI engine operates as a relative identification system, capable of differentiating between two materials, but not necessarily equipped to absolutely identify each material independently.
  • the AI engine may assess whether the material under consideration is the same as a reference material . If a discrepancy or perturbation is detected, indicating that the material is different, this information is relayed to the user, through a notification system. This capability allows for the identification of deviations from expected material properties.
  • the AI engine may not only detect but also report the source of a perturbation throughout the concrete lifecycle.
  • the AI engine may suggest compensatory actions to rectify identified perturbations. For example, the AI engine may recommend adding water or admixture to a material to achieve a desired contextual material property.
  • the AI engine may evaluate the expected impact of these recommended actions, allowing for informed decision-making.
  • the AI engine may detect early anomalies by utilizing the range of devices and models described herein, including sensor devices and context awareness models. By employing sensors throughout the value chain and construction process, the AI engine can facilitate early detection and reporting on the source of perturbations. This provides users with timely insights regarding the causative factors of anomalies, encompassing the ‘when’, ‘what’, and ‘why’.
  • the perturbation and/or anomaly identification and determination system within the AI Engine has several use cases, each used for ensuring the integrity and consistency of materials throughout their lifecycle. These use cases include ID matching, chain of custody matching, and comparison to related historical or contemporaneous pours.
  • the AI engine may be tasked with verifying whether the material currently under consideration matches a target material.
  • the target material could be specified by a user or suggested by another model. This matching process is vital for confirming that the material being used or analyzed is indeed the one intended or expected.
  • the AI engine may track the continuity of a material over time. The system assesses whether the material identified at a later time T′ is the same as the one identified at an earlier time T. An example in the context of concrete would be to ascertain if the material identified in the pour is the same physical volume of concrete that was identified back in the batching plant.
  • the AI engine may compare concrete materials used in different pours at a construction site.
  • the functionality may be distributed across four models, including Expectation Determinator, Perturbation Detector, Perturbation Cause Analyzer, and Compensation Recommendation Engine.
  • the Expectation Determinator may determine material identity or material classification expectations.
  • the user may input a pre-defined target material identity or material class (as defined by its composition, properties, identifier, or other representations), which may become the expected material or class.
  • the model may consider the element type and prior similar elements on the same jobsite, to determine expected material identity or class (by property, composition or otherwise).
  • the model may infer expectations for material identity and/or class for a given scenario (e.g., for different contextual conditions), and by extension, what materials or classes not to expect in those scenarios.
  • the Perturbation Detector may detect or determine material anomalies or material perturbations. Perturbation Detector examines if the material currently being evaluated matches the expected material identity or class. If there is a discrepancy between the two, it indicates a perturbation or anomaly.
  • the Perturbation Cause Analyzer may identify not only the occurrence of anomalies in material identification but also in pinpointing their sources and assessing their impacts.
  • the model may analyze a range of data elements to determine the cause of this discrepancy. Key factors under scrutiny include the timing of the perturbation, the material’s contextual conditions at that time, and any associated actions taken, like the addition of substances to the material . Following the identification of the cause, the model proceeds to evaluate the impact of this anomaly on the material. This evaluation may include quantifying the magnitude of the impact on the material’s properties and composition, including both compositional and contextual material properties. For instance, in the case of extra water in concrete, the model may assess how this affects the material ’s strength, workability, curing time, and overall suitability for its intended application.
  • the Compensation Recommendation Engine may respond to anomalies or perturbations identified in material batches by recommending compensatory actions aimed at correcting identified anomalies and restoring the target or expected properties of the material.
  • the model may propose specific actions that can be taken to counteract the identified issue.
  • the model may also provide predictions about the expected impact of these actions.
  • the output may also include contextual details such as the timing of the recommended compensation action and the associated material contextual conditions during that time.
  • the model may also assess and quantify the magnitude of the expected impact of the recommended actions on the material, covering various properties of the material, including both its compositional and contextual properties.
  • the perturbative mode of the AI Engine may include a time- evolving analysis to uncover the origins of anomalies. This approach involves correlation and causation analysis over time, providing insights into the development and emergence of anomalies.
  • the perturbation detector model not only identifies potential anomalies but also assigns likelihood scores to these predictions.
  • the perturbation cause analyzer may analyze the anomaly’s likely origin, such as unexpected additions during material processing or transportation. This information is then passed on to the compensation recommendation engine (e.g., the processor described herein), which proposes compensatory actions to mitigate the anomaly, ensuring the material still meets the predefined specifications or expectations.
  • the AI engine may be configured to interact with users for enhanced training and adaptability.
  • the machine learning classifier used to detect perturbations may be a supervised learning classifier.
  • the classifier designated as F_i for identification, F_c for classification, and F_p for perturbation, may predict the material type output (Y) from a set of potential multiple identifiers (I), classes (C), or perturbations (P), respectively.
  • Each model equipped with its internal tunable parameters (w_i for identification, w_c for classification, w_p for perturbation), may receive input data (X), which can come in various forms and combinations, with some inputs possibly missing.
  • a classifier F(X;w) aims to maximize the probability that its prediction corresponds to a given class label conditioned on input X.
  • the learned model In the standard classification framework functioning within a supervised learning capacity, the learned model (mapping F), necessitates a rich training dataset for the development of a reliable model.
  • This framework may employ both standard statistical techniques and AI methods to reduce dimensionality and manage the complexity of the data.
  • AI techniques like PCA (Principal Component Analysis), t-SNE (t-distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation and Projection) may be utilized to transform the data into orthogonal bases, simplifying the multi-dimensional data into formats that are easier to analyze and interpret.
  • AI techniques such as encoder-decoder models and autoencoders play a significant role in dimensionality reduction.
  • the AI engine may be capable of extending its applications to an unsupervised classification setting. This extension may be facilitated by coupling the dimensionality reduction with standard clustering techniques like k-means clustering and density-based clustering methods, or with Gaussian material ture models.
  • multi-class labeling may be extensively utilized in machine learning and neural network applications, guided by two key aspects: the variety of data and the integration of physico-chemical models with advanced AI methods.
  • the AI engine may employ stacking techniques that combine different machine learning (ML) and neural network (NN) tools.
  • ML machine learning
  • N neural network
  • ANNs artificial neural networks
  • ANNs may be stacked on top of ensemble learners, such as Random Forests, to process inputs and predict material outputs.
  • meta-regression models stacked onto classifiers can categorize material classes based on human-interpretable material properties.
  • Materials in the AI Engine can be identified and classified with varying degrees of granularity. While the material identification engine and the perturbation detection models primarily operate at the level of individual materials, they may also have the capability to function at broader levels, such as material types or material families.
  • the definition of a ‘material ’ can be expanded by applying broader tolerances in the chosen representation of material -space. For instance, a material formulation may be defined within certain batching tolerances. Adjusting these tolerances can change the level of granularity at which the material is identified. Similarly, in classification systems, the size of the classification volumes within material -space can be varied depending on the desired level of granularity.
  • the concept of a ‘material ’ or a ‘material family’ may not be strictly defined, allowing for various characterizations and classifications of material s and material types.
  • the AI Engine may provide users with the option to choose from a predefined set of granularities and definitions. These definitions could be based on material properties, characteristics, constraints, or chemical composition, and are applicable for both absolute material identifier and perturbation detection. This mode may be particularly useful in situations where batch variability might cause identical materials to be recognized as different.
  • These classifications can include multiple levels, such as percentage bands of GGBS content, ranges of aggregate sizes in the material, or regional and sub-regional origins of the sand. These categories may group materials along various axes and to different degrees of resolution.
  • the training stage is the process by which a model is either created, finalized, and/or updated (where updating only occurs if the model has been trained previously), and where the procedure is designed to ensure the model outputs some minimal requirement of accuracy (where “accuracy” is defined by the details of the training procedure).
  • One example of the training stage being instantiated using simulations as follows: using differential procedures, simulations are run that assess sensitivity of changes in one or more outputs due to complex changes in input parameters and the simulations may discover which inputs have been learned in the training stage to influence the outputs and to what extent the inputs influence the output.
  • the execution stage is the process by which a pre-trained model is used for its intended purpose, where the intended purpose is defined by the goals of the training procedure.
  • Explanations of methods used by models of the present disclosure may be directed to either the form and scope of the functions used in the execution stage or the algorithmic choices by which models may be trained in the training stage.
  • the distinction between training and execution is significant, as it is possible for two models to execute the same function types, but for the two models to have been trained via different training procedures. Generally, two identical sets of functions trained via different procedures may possess a different degree of accuracy, precision, and/or scope, even if trained on the same data.
  • the model When a model is created at the training stage, the model’s internal mapping between input and output variables may be altered (typically in an iterative process).
  • the internal representation may be represented by a set of numbers that are referred to as “weights”, and which define both the weighted combination of internal computations, and/or the functional computations themselves.
  • the training stage is the process by which the weights of a model are modified so as to minimize or maximize an “objective function”.
  • the objective function may be defined such that the objective function receives an input/output pair (where the output is derived from executing the model in its current state, for the given input), and may calculate the “error” between the current output and the desired output of the model.
  • the desired output of the model may be provided to the objective function via historical instances of input-output pairs.
  • the training method may be supervised, and the objective function may directly compare the output of the model with the desired output.
  • the objective function may calculate a derived quantity of the input and/or output and may measure the deviation of that derived quantity from the desired derived quantity.
  • These algorithms are typically called unsupervised algorithms and may create useful models without the need for having prior examples of desired outputs that pair with inputs.
  • One example of an unsupervised objective function is a function that minimizes the cross-entropy between sets of calculated distributions on the outputs of the model (creating a maximally informative classification system, relative to the data and the internal model representation).
  • a non-exhaustive list of execution models used by any of the models described herein may include physical and/or chemical models, tree-based methods, decision trees, bagging trees, random forests, gradient boosted trees, Bayesian probability theory, artificial neural networks, convolutional networks, recurrent neural networks, transformers, generative adversarial networks, diffusion systems, clustering algorithms, K-means clustering, hierarchical clustering, density based spatial clustering, spectral clustering, affinity propagation, Gaussian mixture models, embedding and/or dimensionality reduction methods, principal component analysis (PCA), independent component analysis (ICA), multi- dimensional compression methods, Bayesian networks, causal graphs, and/or ensemble models.
  • PCA principal component analysis
  • ICA independent component analysis
  • Bayesian networks Bayesian networks
  • causal graphs and/or ensemble models.
  • Discrete and Continuous Data may be able to receive and process an arbitrary assortment of discrete and continuous data.
  • discrete data may include names (e.g., of materials, places, devices, physical elements, etc.), True/False values (e.g., whether a pour contains a device of certain type), arbitrary text (e.g., comments written by users on a form), and/or time zone information (e.g., UTC, GMT, etc.).
  • continuous data may include temperature (e.g., in units of Celsius), dimensions (e.g., in units of meters or inches), density (e.g., in units of kg per cubic meter, time (e.g., in units of seconds).
  • the four categories of measurement data described as described herein may each contain both discrete and continuous data.
  • Types of Continuous Data [00806] With regards to the models that are described herein, continuous data may be defined as being of two possible categories, point values or range values. A point value may be an instance of a datum of the continuous type that has one exact value. For example, a temperature of 23.4 Celsius is a point value of temperature.
  • a range value may be an instance of a continuous data type that has a lower and upper bound and may not contain a specific point value within that range.
  • the values between 20 Celsius and 30 Celsius may be an instance of a range value of temperature.
  • the models herein may be capable of outputting both point values or range values, and the circumstance under which a point or range value is output may depend upon the specifics of the solution being provided by the model.
  • a probabilistic model that is designed to calculate the uncertainty on a measurement or calculation will output both point and range values at once, where the point value could be a statistical mean, and the range value could be the upper and lower bound on the spread to within a 99.9% confidence.
  • the function, f may not be analytically differentiable, but a first-order linearized local differential may be computed over some input domain.
  • the above equation may be capable of propagating a shift, ⁇ ⁇ , of the i-th input variable into a shift, ⁇ , in the output.
  • Graph models establish a set of weighted and directional connections between nodes, where those weighted connections represent a multiplicative modification to the node values, and where that multiplicative modification is applied by traversing the graph between nodes.
  • Graphs used by any of the models described herein may generally be directed and may be permitted to contain cycles of arbitrary number or degree (including self-connections at nodes).
  • the weights used in graph models are either measures of probability (for the Bayesian networks) or measures of causal direction / causal impact (for the causal graphs). Hybridized graphs, where some weights are measures of probability and some are measured of causal impact may also be used.
  • the first model may be configured to predict properties of the target material under a second pressure and a second temperature, a first pressure and a second temperature, and/or a second pressure and a first temperature.
  • a set of contextual conditions may include a plurality of contextual conditions.
  • Options for contextual conditions may include, but are not limited to, temperature, pressure, humidity, chemical environment, electric field, magnetic field, strain rate, radiation, frequency of applied forces, time, concentration of impurities, surface roughness, loading conditions, environmental exposure, vibration, thermal cycling, phase composition, microstructure, loading duration, anisotropy, phase transformation temperature, and/or the like.
  • the first model may be configured to constrain an output of the first model to the input set of contextual conditions. Further, if no set of contextual conditions is input, the first model may be configured to set a default value for each contextual condition of a set of contextual conditions and/or ignore one or more contextual conditions as the first model simulates the material composition data. [00846] As shown in block 3006, the flowchart 3000 may include the step of generating one or more material properties, via the simulation, of the target material in the second set of contextual conditions.
  • the target material may include material properties that may vary under different sets of contextual conditions (e.g., conductivity of a material may vary with temperature).
  • the one or more material properties generated by the first model may be an exhaustive set of material properties (e.g., any possible property a material could have) of a target material. Additionally, and/or alternatively, the one or more material properties generated by the first model may be a non- exhaustive set of material properties (e.g., the AI model may be designed for certain material properties) of a target material. [00847] As shown in block 3008, the flowchart 3000 may include the step of generating a first output including one or more first output data entries associated with the one or more material properties for the target material in the second set of contextual conditions. In some embodiments, the one or more first output data entries associated with the one or more material properties may include the output of the simulation conducted by the first model.
  • the flowchart 3100 may include the step of providing material property data in one or more sets of contextual conditions to a first model, where the first model is an artificial intelligence (AI) model.
  • the first model may be configured to receive a plurality of data including the material properties in the one or more sets of contextual conditions as inputs. Further, the inputs may be directed to one or more materials. Additionally, and/or alternatively, the first model may be configured to generate material identifiers associated with materials that may yield the same material properties in the one or more sets of contextual conditions.
  • the first model may be configured to default to a value for a contextual condition if the value for the contextual condition is absent, operate without using the contextual condition as input if the value for the contextual condition is absent, and/or generate a value for the contextual condition if the value for the contextual condition is absent.
  • the first model may be developed under one or more algorithms including supervised learning (e.g., using logistic regression, using back propagation neural networks, using random forests, decision trees, etc.), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering), semi-supervised learning, reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning), natural language processing (e.g., Q-learning, deep Q-networks (DQN), policy gradients, proximal policy optimization (PPO), actor-critic methods, etc.), and/or any other suitable AI model type as described herein.
  • supervised learning e.g., using logistic regression, using back propagation neural networks, using random forests, decision trees, etc.
  • unsupervised learning e.g., using an Apriori algorithm, using K-means clustering
  • semi-supervised learning e.g., using a Q-learning algorithm, using temporal difference learning
  • natural language processing e.g., Q-learning, deep Q-networks
  • Each of these types of AI algorithms may implement any of one or more of a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self- organizing map, etc.), a regularization method (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, etc.), a decision tree learning method (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, chi-squared automatic interaction detection, decision stump, random forest, multivariate adaptive regression splines, gradient boosting machines, etc.), a Bayesian method (e.g., na ⁇ ve Bayes, averaged one-dependence estimators, Bayesian belief network, etc.), a kernel method (e.g., a support vector machine, a radial basis function, etc.), a clustering method (e
  • the first model prior to receiving the data, may be trained on data including one or more material identifiers, one or more sets of compositional data, and/or one or more sets of material properties in one or more sets of contextual conditions as described herein.
  • the flowchart 3100 may include the step of simulating, via the first model, the material property data.
  • the inputs to the first model may be used in the simulation to generate outputs of data directed to generating material identifiers of materials that satisfy the material properties of material property data. Further, the generated material identifiers may be associated with a target material that is stored in one or more databases.
  • the generated material identifiers may be associated with a candidate material (e.g., a material and/or material mix not presently known).
  • the first model may be configured to generate material composition data for the candidate material (e.g., the flowchart 3000 as shown and described herein with respect to Figure 30).
  • a set of contextual conditions may include a plurality of contextual conditions.
  • Options for contextual conditions may include, but are not limited to, temperature, pressure, humidity, chemical environment, electric field, magnetic field, strain rate, radiation, frequency of applied forces, time, concentration of impurities, surface roughness, loading conditions, environmental exposure, vibration, thermal cycling, phase composition, microstructure, loading duration, anisotropy, phase transformation temperature, and/or the like.
  • the flowchart 3100 may include the step of generating one or more material identifiers, via the simulation, corresponding to the material property data.
  • the results of the simulation may identify a likelihood that one or more target materials may satisfy the conditions set forth by the material property data in one or more sets of contextual conditions input into the first model.
  • the first model may be configured to generate, for each identified material, one or more material identifiers for each identified material.
  • the flowchart 3100 may include the step of generating a first output including one or more first output data entries associated with the one or more material identifiers.
  • a first output may be generated including the material identifiers.
  • the one or more material identifiers in the one or more sets of contextual conditions may be processed post simulation and/or prior to incorporation into the first output.
  • the first output may be transmitted for storage (e.g., in the memory 206 as shown and described herein with respect to Figure 2).
  • the flowchart 3100 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein. Although Figure 31 shows example blocks of the flowchart 3100, in some embodiments, the flowchart 3100 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 31. Additionally, or alternatively, two or more of the blocks of the flowchart 3100 may be performed in parallel.
  • Figure 32 illustrates a flowchart 3200 for an example method for generating material composition data, in accordance with some embodiments of the present disclosure.
  • the operations illustrated in Figure 32 may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., the server 200 as shown and described herein with respect to Figure 2), as described above.
  • performance of the operations may invoke one or more of processor 202, memory 206, communication interface 204, and/or artificial intelligence (AI) module 208 as shown and described herein with respect to Figure 2.
  • AI artificial intelligence
  • the flowchart 3200 may include the step of generating a plurality of material composition data for each candidate material identifier of one or more candidate material identifiers.
  • an AI model e.g., a first model as described herein
  • the AI model may be configured to analyze the candidate material identifier and generate a plurality of material composition data for the candidate material identifier. Additionally, and/or alternatively, the plurality of material composition data for the candidate material identifier may be generated via a comparison to similar and/or related material identifiers that may have data stored in one or more databases. In some embodiments, the candidate material identifiers may be generated by the steps of flowchart 3100 as shown and described herein with respect to Figure 31. [00860] As shown in block 3204, the flowchart 3200 may include the step of generating a first output including one or more additional first output data entries associated with the plurality of material composition data for each candidate material identifier.
  • Figure 33 illustrates a flowchart 3300 for an example method for generating compositional arrangements for candidate material identifiers in accordance with some embodiments of the present disclosure.
  • the operations illustrated in Figure 33 may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., the server 200 as shown and described herein with respect to Figure 2), as described above.
  • performance of the operations may invoke one or more of processor 202, memory 206, communication interface 204, and/or artificial intelligence (AI) module 208 as shown and described herein with respect to Figure 2.
  • AI artificial intelligence
  • the flowchart 3300 may include the step of generating a compositional arrangement for a plurality of material composition data for each candidate material identifier of one or more candidate material identifiers, where the compositional arrangement includes a sequence of chemical equations for each of the candidate material identifiers.
  • a first output may include one or more material identifiers generated via a simulation of material property data conducted by a first model (e.g., similar to the steps of the flowchart 3100 as show and described herein with respect to Figure 31).
  • Said one or more material identifiers may include the one or more candidate material identifiers.
  • the plurality of material composition data may be generated for each candidate material identifier of the one or more candidate material identifiers (e.g., similar to the steps of the flowchart 3200 as shown and described herein with respect to Figure 32).
  • the plurality of material composition data may then be, in some embodiments, ingested by a model configured to generate compositional arrangements for materials.
  • the model may generate for each candidate material identifier a compositional arrangement that includes a sequence of chemical equations associate with each candidate material identifier.
  • the flowchart 3300 may include additional embodiments, such as any single embodiment or any combination of embodiments described herein.
  • Figure 33 shows example blocks of the flowchart 3300
  • the flowchart 3300 may include additional blocks, different blocks, or differently arranged blocks than those depicted in Figure 33. Additionally, or alternatively, two or more of the blocks of the flowchart 3300 may be performed in parallel.

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Abstract

L'invention concerne des systèmes, des produits-programmes informatiques et des procédés pour des caractérisations associées à un matériau quantique. En référence à un procédé donné à titre d'exemple, le procédé fait interagir un ou plusieurs états quantiques avec un matériau cible, génère un ou plusieurs premiers éléments de données sur la base d'une mesure associée à l'interaction du ou des états quantiques avec le matériau cible et délivre le ou les premiers éléments de données. Le procédé détermine en outre un changement dans au moins un état quantique du ou des états quantiques après l'interaction avec le matériau cible, génère un ou plusieurs seconds éléments de données associés à une caractéristique du matériau cible sur la base du changement déterminé dans le ou les états quantiques et délivre le ou les seconds éléments de données.
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