881 results about "Molecular dynamics" patented technology

The invention discloses an asphalt pavement fatigue damage model calibration method based on AI and digital twinning, and relates to the technical field of damage detection. Compared with the prior art, the problems that traditional asphalt pavement design depends on empirical formulas and static parameters, material aging, environmental coupling and load uncertainty are difficult to dynamically reflect, and cross-scale correlation between microscopic interface behaviors and macroscopic structure performance is lacked are solved; the method comprises the following steps: collecting and preprocessing multi-source heterogeneous data in real time through a multi-modal data fusion and dynamic sensing system, simulating and accurately quantifying asphalt-aggregate interface binding energy and adhesion work in combination with molecular dynamics, and constructing a cross-scale damage evolution model; a physical information neural network is used for embedding an improved Paris formula, actually measured data and a physical rule are deeply fused, and the locality hypothesis of a traditional model is effectively corrected.

The invention provides a gradient crosslinking process of photosensitive resin for photocuring 3D printing, and relates to the technical field of photocuring 3D printing materials. The gradient crosslinking process of the photosensitive resin for photocuring 3D printing comprises the following steps: S1, based on double-initiator system design, adopting molecular dynamics simulation screening, respectively adding a 405nm sensitive type TPO-L initiator and a 365nm sensitive type I TX initiator into an epoxy acrylate prepolymer, and verifying initiation energy barrier matching through quantum chemistry calculation, and generating a pre-initiation gradient resin formula. Through molecular dynamics simulation, initiator combinations are screened, energy barrier matching is verified, the ratio of dual-wavelength sensitive initiators is optimized, and accurate triggering of surface layer and deep layer cross-linking reaction is ensured. And a three-roller grinding and dispersing process is combined with ultrasonic oscillation to eliminate nanoparticle aggregation, so that the mechanical uniformity and light scattering effect of a resin system are improved.

The invention discloses a chalcogenide phase change storage material potential model construction method, a material characteristic calculation method and electronic equipment, and belongs to the field of semiconductor materials. On the basis of a potential model trained by a first training set collected through molecular dynamics simulation based on a first principle calculation method, a current potential model is fully utilized, molecular dynamics simulation is carried out based on the current potential model, training samples are further collected, representative training samples are screened out, and a second training set is formed; according to the method, the training efficiency is high, the generalization ability is high, and a large amount of training data can be obtained with low cost. Meanwhile, verification of physical properties is introduced into convergence conditions of model training, so that the physical consistency and interpretability of the potential model are improved; on the basis, the training set which is good in diversity and sufficient in data volume can be obtained with low cost, and meanwhile the accuracy of the potential model in an actual application scene is further improved.

The invention discloses a dynamic optimization system and method for a compound essential oil ratio, and aims to solve the problems that the traditional technology depends on artificial experience, the optimization efficiency is low and the quality tracing is insufficient. According to the method, raw material molecular fingerprint data are collected in real time through a high-precision spectrum sensor array and a micro-fluidic chip, a production environment field domain is constructed in combination with a computational fluid mechanics model, a formula optimization problem is encoded into an Ising model by using a quantum annealing algorithm, and global search is realized through a quantum tunneling effect. The system integrates digital twin bodies, covers molecular dynamics simulation, phase equilibrium prediction and olfactory receptor activation probability simulation, and accurately maps a physical system state; a multi-modal data fusion network and a reinforcement learning framework are adopted to dynamically adjust formula parameters, and an NSGA-III algorithm is combined to optimize efficacy collaboration degree, cost and stability indexes. And the execution control module realizes nanoscale flow regulation through the piezoelectric micro-injection valve array, and realizes non-tampering evidence storage of formula parameters and quality data based on a block chain technology. Dynamic closed-loop optimization of the compound essential oil ratio is achieved, the production efficiency, the product consistency and the supply chain transparency are remarkably improved, and the method is suitable for the field of intelligent manufacturing of high-end essential oil.

The invention discloses a cross-scale rock elastic mechanical parameter measurement method and system, and relates to the technical field of rock measurement, and the method comprises the following steps: obtaining nano-scale rock elastic mechanical parameters and micro elastic-plastic deformation of rock through a nanoindentation experiment, so as to construct a molecular dynamics model of the rock; according to the molecular dynamics model of the rock, using an inclusion homogenization theory to upgrade the rock elastic mechanics parameters of the nanoscale to the rock elastic mechanics parameters of the centimeter scale, so as to construct a rock elastic mechanics parameter conversion model, and using environmental data in an in-situ environment for correction; and inputting the nano-scale rock elastic mechanical parameters into the corrected rock elastic mechanical parameter conversion model to obtain centimeter-scale rock elastic mechanical parameters. According to the invention, centimeter-scale rock elastic mechanical parameters in a deep stratum in-situ environment can be accurately measured.

The invention provides a method and a system for evaluating the luminescence performance of a near-infrared two-region molecular fluorescent probe, and relates to the technical field of fluorescent probes, the method comprises the following steps: obtaining the initial configuration of the near-infrared two-region molecular fluorescent probe in an aqueous solution, and carrying out molecular dynamics simulation on the initial configuration to obtain a plurality of representative conformations of the probe in the aqueous solution; performing quantum chemical calculation on each representative conformation to obtain first system energy of each representative conformation in a stable state and second system energy and intensity parameters of each representative conformation in a lowest singlet excitation state; generating a comprehensive evaluation factor based on the first system energy, the second system energy and the intensity parameter; and sorting the luminescence properties of the probes based on the comprehensive evaluation factor, and screening out the probe with the optimal performance. According to the method provided by the invention, quantitative evaluation on the luminescence property of the near-infrared two-region molecular fluorescent probe is realized, and technical support is provided for optimal design and clinical application of the probe.

The invention provides a metal matrix composite microstructure strengthening simulation method and system, and relates to the technical field of composite mechanical property simulation, and the method comprises the following steps: establishing a polycrystalline aluminum matrix atomic model and a spherical silicon nitride particle strengthening phase atomic model unit; embedding the spherical silicon nitride particles into the polycrystalline aluminum matrix according to the target embedding position coordinates, and integrating to generate an atomic scale structure model; according to the method, an embedded atom potential function is adopted to describe interaction among atoms in a polycrystalline aluminum matrix, a Tersoft potential function is adopted to describe covalent bond interaction among atoms in spherical silicon nitride particles, and a Lennard-Jones potential function is adopted to describe interaction among heteroatoms at a Si3N4 / Al interface; based on the interaction potential between atoms, molecular dynamics simulation is adopted for conducting uniaxial stretching simulation on the atomic scale structure model, mechanical property parameters of the particle reinforced metal matrix composite are obtained through calculation, and a visualization method is combined for analyzing the microcosmic strengthening mechanism of the particle reinforced metal matrix composite.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

A system and method are provided for a machine-learning drive framework for predicting ionic conductivity of solid-state electrolytes. In use, the method and / or system may include receiving, at a machine learning system, two or more molecular structures from at least one structural dataset, where the two or more molecular structures relate to ionic mobility. Additionally, atomic weights are calculated for the two or more molecular structures, and the machine learning system is trained based on the two or more molecular structures, where the training relies on at least one intrinsic atomic feature and the calculated atomic weights for the two or more molecular structures. Further, a bias-correction is applied for the two or more molecular structures to improve the training of the machine learning system. Further, one or more molecular dynamics (MD) simulations are outputted, using the machine learning system, for the two or more molecular structures.

The invention relates to multi-scale modeling and mechanical and thermodynamic property simulation of a particle reinforced biological composite material, and belongs to the crossing field of biology, material science and computer simulation. The method specifically comprises the following steps: constructing a polyether-ether-ketone (PEEK) high-molecular polymer and a graphene reinforced composite material model thereof through a cross-linking algorithm under a microscopic scale, and simulating and calculating the mechanical and thermodynamic properties of the model by adopting molecular dynamics; in the microscopic scale, constructing a representative volume element model of the microstructure artificial skeleton, inputting property parameters calculated by molecular dynamics into a microscopic model, and calculating microscopic mechanical and thermodynamic properties of the model by adopting a substance point method; and inputting the property parameters obtained under the microscale into the macroscopic model, and calculating the overall macromechanical and thermodynamic behavior response of the material by adopting a finite element method. According to the method, mechanical and thermodynamic responses of the biological composite material are finally simulated through transmission of property parameters of different scales, and theoretical support is provided for research and formula design of the particle reinforced biological composite material.

In a small molecule drug discovery method, a transition state for a specific enzyme is modelled using quantum mechanics and molecular dynamics based simulation of the enzyme and substrate reaction; data defining the transition state (a ‘quantum pharmacophore’) is fed to a machine learning engine configured to generate transition state analogues, such as enzyme inhibitors.

The invention provides a classroom simulation method for virtual reality, and relates to the technical field of data processing, and the method comprises the steps: obtaining a high-dimensional data matrix with a time-space correlation characteristic; constructing an electrical connection relation graph; obtaining a quantum state evolution result of the transient process of the power system; calculating to obtain a multi-physical field distribution parameter corresponding to the equipment; based on the multi-physics field distribution parameters, combining molecular dynamics simulation and a fluid mechanics model to obtain arc dynamic characteristic data; collecting an electromyographic signal of an operator, and generating a force feedback control instruction through a neural network mapping model in combination with mechanical parameters of equipment; and according to the eye movement tracking data, the arc dynamic characteristic data and the force feedback control instruction, generating a virtual reality scene adaptive to the visual response and the operation state of the operator by using a dynamic rendering engine. According to the embodiment of the invention, the authenticity and interactivity of virtual reality classroom simulation can be improved.

The invention relates to the technical field of carbon dioxide sequestration and shale gas development, and particularly discloses a shale micro-nano pore multiphase multicomponent competitive adsorption data determination method and device, and the method comprises the steps: obtaining a shale core sample of a target area, and determining the type and relative content change characteristics of surface functional groups of the shale core sample; key structures and force field parameters of shale clay minerals in a target block are collected, and a shale clay mineral molecular model is established; establishing a plurality of fluid molecular models; establishing slit models, and establishing a multiphase fluid system model between the slit models to obtain a stable configuration; applying an external electric field to the stable configuration, and performing molecular dynamics calculation to obtain molecular trajectory coordinates and dynamics information of the multi-phase multi-component fluid; and calculating competitive adsorption data of the shale micro-nano pore multi-phase multi-component fluid under the action of the electric field. According to the scheme, the occurrence state and the competitive adsorption behavior of the shale micro-nano pore multi-phase multi-component fluid under the action of the electric field can be disclosed from the molecular scale.

The invention relates to the technical field of data processing, in particular to a liquid disperse dye for polyester fabrics and a use method thereof, and the dye comprises an azo or anthraquinone disperse dye main agent subjected to superfine grinding, a nonionic and anionic compound dispersant system, a fiber plasticizer and a stabilizer. According to the preparation method, molecular dynamics simulation and mesoscale dissipative particle dynamics simulation are combined, the synergistic effect of a dispersant adsorption configuration and double electric layer potential is quantified, and a nonlinear association rule of dynamic particle size control and dispersion stability is established; a multi-physics field coupling model is adopted to integrate fiber humidity, temperature gradient and pressure distribution parameters, and cross-scale collaborative optimization of microscopic interface action and macroscopic process parameters is achieved. According to the method, repetitive experiment iteration is replaced by digital design, the formula development period is remarkably shortened, the dyeing uniformity and the batch-to-batch reproducibility are improved, and the method is suitable for continuous dyeing production of high-density and superfine polyester fabrics.

The invention relates to the technical field of researching or analyzing materials by testing chemical or physical properties of materials, in particular to an intelligent analysis method for corrosivity of sulfide molecules in transformer oil. The influence of the oil environment on the adsorption energy is simulated and corrected in combination with molecular dynamics, a microscopic parameter database is constructed, and a corrosion risk grade classification rule and a generation rate prediction model are established. Oil data are collected in real time and input into the model to output a dynamic risk level, and a circulation rate adjusting or corrosion inhibitor adding instruction is triggered. Model parameters are regularly corrected by combining a closed-loop feedback mechanism, and a microscopic database is expanded, so that transfer learning prediction of unknown sulfide corrosion behaviors is realized. According to the method, the limitation of an existing single experiment condition is broken through, the relevance between a microcosmic molecular mechanism and a macroscopic corrosion behavior is dynamically analyzed, and the prediction precision under the multi-factor coupling effect is improved.

The invention discloses an AIREBO potential optimization method and system suitable for a multi-core processor, and relates to the technical field of molecular dynamics simulation. The method comprises the steps that atomic information is acquired, and a layered adjacency list is constructed according to the truncation radius characteristic of the AIREBO potential and the atomic information; a key sequence network is pre-constructed according to connectivity information between atoms in the adjacency list, and the pre-constructed key sequence network is utilized when the FLJ function is calculated; classifying and grouping according to different types of interactions among atoms, calculating FREBO functions in batches according to a classifying and grouping result, splitting calculation tasks, and processing the split tasks by using a multi-core processor; and integrating the calculation results of the FLJ function and the FREBO function, and combining the calculation result of the FORRSION function to obtain the final AIREBO potential. According to the method, AIREBO potential efficient parallel computing is realized on the basis of a multi-core processor architecture, and the overall molecular dynamics simulation efficiency is improved.

The invention discloses a lubricant adsorption and friction regulation and control method based on molecular dynamics simulation, which comprises the following steps: S1, constructing a lubricant molecule and friction pair metal unit cell model, generating topological structure information based on a minimum structure unit, and constructing a lubrication system model with a sandwich structure; s2, giving force field parameters to the lubrication system model, and performing geometric structure optimization and dynamic relaxation; s3, giving surface charges to the upper and lower friction pairs for targeted adsorption simulation of the lubricant; and S4, applying uniformly distributed contact pressure and constant sliding speed to the upper friction pair to perform friction simulation of the lubrication system. According to the lubricant adsorption and friction regulation and control method based on molecular dynamics simulation, a targeted adsorption strategy of a solid-liquid interface is established, the microcosmic adsorption process of the lubricant is reproduced, controllable regulation of friction characteristics is achieved, the human resource cost in actual experiments is reduced, and manual operation errors are reduced.

The invention provides a drinking water pipeline anti-corrosion powder coating multi-component proportioning intelligent batching method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the following steps: obtaining drinking water pipeline anti-corrosion powder coating raw material parameters and formula information; constructing a quantitative characteristic volume element numerical simulation unit, and performing multi-scale bridging calculation in combination with microscopic molecular dynamics and a macroscopic discrete element method; inputting parameters, calculating, correcting a formula, and converting into control parameters; dynamically adjusting parameters through a regression network self-learning feedback compensation algorithm; starting mixing equipment for batching, calculating the uniformity in real time, stopping mixing after the uniformity reaches the standard, detecting a finished product, and generating a report. According to the invention, through a self-learning feedback compensation algorithm based on a regression network, in combination with real-time weight detection and a material blockage identification result, intelligent dynamic adjustment of quantitative feeding control parameters is realized.

The invention discloses a microemulsion interfacial tension efficient prediction method and system based on active learning and molecular dynamics. According to the method, 217 molecular descriptors corresponding to each molecular structure are calculated by adopting an RDKit software package, and the descriptors are used for representing molecular structure characteristics and serve as input variables of a machine learning model, so that key structure information including molecular branching degree, polarity and the like is transmitted. For an oil-water-surfactant ternary interface system, the oil-water interfacial tension in the presence of a surfactant is simulated and calculated through molecular dynamics, and an IFT value is set as a model prediction target. An active learning mechanism is introduced, and iterative sample labeling in the molecular dynamics simulation process is guided; and integrating the obtained IFT data with the molecular descriptor features, constructing a machine learning data set, and training a random forest model. According to the method, the problem of screening a high-performance surfactant layer by a middle-phase microemulsion system can be solved, and the ultra-low oil-water interfacial tension can be rapidly and efficiently screened.

The invention provides a design method of a protein variant structure based on molecular dynamics, and belongs to the technical field of molecular dynamics simulation. A protein-standard nucleic acid compound ground molecule system and a protein-nonstandard sequence compound ground molecule system are respectively constructed, and based on a molecular dynamics simulation technology, protein conformation stability, low-energy conformation states, main change processes, collaborative movement and key residues of the protein-standard sequence system and the protein-nonstandard sequence system are compared, so that protein conformation stability and low-energy conformation states of the protein-standard nucleic acid compound ground molecule system and the protein-nonstandard sequence compound ground molecule system are obtained. The molecular mechanism of protein-nucleic acid interaction is analyzed, the recognition behavior of the protein on the standard nucleic acid sequence and the activation process of the standard nucleic acid sequence on the corresponding function of the protein are understood from the molecular level, and finally the protein variant with more related functions is designed.

The invention discloses an energetic crystal surface hydrophobicity evaluation method based on an improved adhesion energy model. The energetic crystal surface hydrophobicity evaluation method comprises the steps that 1, crystal habit information of a specified CL-20-based energetic eutectic in vacuum is simulated and predicted through molecular dynamics; 2, cutting a surface structure corresponding to the crystal habit, and constructing a molecular scale eutectic-solvent interface model; step 3, correcting the crystal face adhesion energy by using the correction adhesion energy model, and simulating and predicting the crystal morphology of the eutectic crystal in a specified solvent; 4, constructing a molecular scale eutectic-water interface model, and evaluating hydrophobicity of different surfaces according to the corrected adhesion energy model; and 5, synthesizing the surface hydrophobicity and the simulated and predicted crystal morphology under different solvents, and screening a proper solvent to realize the energy-containing eutectic hydrophobicity regulation and control. According to the method, accurate quantification of hydrophobicity is realized by dynamically simulating interface energy change.

The invention relates to the technical field of thermal protection of hypersonic aircrafts, in particular to a multi-scale prediction method for an ablation behavior of a thermal protection structure based on data driving, which comprises the following steps: performing molecular dynamics simulation on a gas-solid interface of an ablation type thermal protection material to obtain simulation calculation activation energy; obtaining the experimental activation energy of the material; performing Bayesian optimization on the simulation calculation activation energy and the experiment measured activation energy to obtain optimized activation energy; performing macroscopic wall surface ablation simulation by taking the optimized activation energy as a boundary condition to obtain an ablation parameter database recording ablation wall surface temperatures, ablation rates and ablation depths corresponding to different flow rates, altitudes and time; training a radial basis function neural network based on the ablation parameter database, and establishing an ablation prediction model; obtaining a prediction result; the calculation efficiency and accuracy of the thermal protection structure ablation behavior multi-scale prediction method can be improved.

The invention belongs to the field of glass material research, and discloses a method for regulating and controlling rare earth ion clusters in laser glass based on molecular dynamics simulation and application of the method. Based on given glass components, density and atomic number, an initial amorphous structure model M0 is constructed; based on a molecular dynamics theory, performing high-temperature melting quenching simulation on the initial amorphous structure model M0, and constructing a glass structure model M; calculating glass microstructure parameters and designing rare earth ion cluster parameters based on atomic coordinate information of the glass structure model M; establishing a glass component-microstructure mathematical model and a microstructure-rare earth ion cluster mathematical model based on the glass components, the microstructure parameters and the rare earth ion cluster parameters; based on the model, regulation and control on the agglomeration degree of the rare earth ions in the glass are realized. According to the method, the problem that the rare earth ion clusters in the laser glass cannot be quickly and effectively regulated and controlled due to the fact that the rare earth ion clusters in the glass are difficult to quantitatively describe by experimental means is solved.

A system and method for generating thermostable variants of a protein is disclosed. The system receives a three-dimensional structure of a target protein and identifies mutable regions, including solvent-exposed residues and loop regions. Conserved and active site residues are excluded from mutation through a fixed-position mask. A message-passing neural network (MPNN) generates mutant sequences at unmasked positions, executed under multiple temperature parameters. Design scores based on Shannon entropy and log probability are computed, and high-confidence variants are selected. Predicted structures for selected variants are evaluated using structural and sequence-based features to compute stability scores. A ranked list of thermostable variants is generated. Top candidates undergo molecular dynamics simulations to compute dynamic metrics such as RMSD, radius of gyration, SASA, and ddG, and are re-ranked accordingly. The system enables accurate, constraint-driven protein design with high structural and functional fidelity, suitable for industrial and therapeutic applications.

The invention relates to the technical field of microcosmic acoustic nonlinear effect simulation, and particularly discloses a method for analyzing the influence of pearlite spheroidization on ultrasonic harmonics based on molecular dynamics, which comprises the following steps: firstly, constructing a pearlite undamaged model meeting a Bagaryatskii orientation relationship and a plurality of spheroidization damage models representing different spheroidization damage degrees; carrying out molecular dynamics simulation on the eight constructed models, and carrying out energy minimization and relaxation treatment to obtain a spheroidized model with a stable structure; applying ultrasonic excitation signals V1 and V2 with opposite phases to each spheroidizing damage model to obtain displacement signals after ultrasonic propagation in different areas; and finally, processing the displacement signal by adopting a phase inversion method, extracting second harmonics, analyzing the change rule of the amplitude of the second harmonics, and establishing an incidence relation between the pearlite spheroidizing damage and the ultrasonic nonlinear effect. Through molecular dynamics simulation and a phase reversal method, the influence of pearlite spheroidization on ultrasonic harmonic waves is disclosed, and accurate theoretical support and method reference are provided for nondestructive testing of the pearlite spheroidization.

The invention belongs to the field of computational chemistry, and particularly relates to a multi-parameter collaborative dynamic screening method for adsorbed species on the surface of a catalyst. Comprising the following steps that an initial crystal structure is constructed and optimized through VESTA software, and a stable crystal structure model is obtained through density functional theory optimization; integrating working condition parameters such as a solvent, pH and potential, and drawing a Bayer diagram to label adsorption species and a mapping relation; and calculating a zero charge point and capacitance to obtain electrochemical parameters, and determining stable adsorption species under actual reaction through constant potential molecular dynamics simulation. According to the scheme, a deep structure-activity relationship between an active site structure and catalytic activity under a working condition is established through integration of all working condition parameters, labeling of adsorption species by a Boubye diagram, constant potential molecular dynamics simulation and the like; the problems that a systematic method combined with actual working condition parameters is lacked in prediction of the adsorbed substances on the surface of the catalyst, and real adsorbed species are difficult to accurately screen are solved, and accurate capture and efficient screening are achieved.

The invention relates to the technical field of computers, in particular to a multi-scale automatic analysis method for an amorphous material atomic structure. Comprising the following steps: S1, taking a reactant as an initial unit, and obtaining a cluster unit of a reaction product based on first principle molecular dynamics simulation; s2, carrying out unit cell construction on the cluster unit, and optimizing unit cell parameters and atomic coordinates of the cluster unit through calculation according to a first principle to obtain unit cell units; s3, spatial stacking is carried out on the unit cell units, unit cell parameters and atomic coordinates of the unit cell units are optimized through calculation according to a first principle, and supercell units are obtained; s4, performing first principle molecular dynamics simulation on the supercell unit to obtain an accurate machine learning potential function; s5, carrying out cell expansion construction on the supercell unit, carrying out molecular dynamics simulation by using an accurate machine learning potential function, and obtaining an amorphous material atomic structure; and S6, packaging the steps S1 to S5 into an automatic workflow. The method is high in efficiency and small in error.

The invention relates to a high-throughput screening method and system for a self-assembled single-layer thermal interface material and a medium. The high-throughput screening method comprises the following steps: constructing an interface heat transport simulation system based on a self-assembled monomolecular layer; screening set preparation is carried out on the end group structure, and an end group screening set and a pre-calculation set are constructed; carrying out molecular dynamics simulation on the self-assembled monomolecular layer corresponding to the pre-calculation set to obtain a physical association index; training a machine learning model by taking the physical association index as a machine learning objective function; carrying out machine learning prediction on the uncalculated screening candidate data set based on the trained machine learning model, and carrying out parallel screening of physical association indexes to obtain a simulation verification set; and carrying out molecular dynamics simulation for verification. Compared with the prior art, high-throughput screening of the self-assembled single-layer thermal interface material is achieved, and the method has important significance on solid-liquid interface heat conduction characteristic regulation and control design under data driving.

The present disclosure provides a system for atomistic-based sensor fusion inference. In operation, based on sensor data (such as hyperspectral data), the systems and methods disclosed herein may be used for more advanced and accurate methods for geological sample analysis and prediction of atomic properties. For example, as disclosed, the system may provide an atomistic-based sensor fusion inference. A geological sample may be received, and a neural network prediction may be applied comprising at least one of an electronic structure or molecular dynamics. The prediction may be used to create an atomistic-based inference of a distribution of underlying geophysical resources located proximate to the sample based on the machine-learned data set of the neural network and the sensor data. This system provides a comprehensive approach to geophysical exploration and resource extraction.

The invention belongs to the technical field of polymer composite materials, and discloses a carbon fiber interface modifier aided design method and system based on collaborative optimization, and the method is used for designing a carbon fiber interface modifier based on collaborative optimization of machine learning and molecular dynamics simulation. By constructing a closed loop iteration process of generation-prediction-verification-learning, interfacial modifier molecules meeting multi-target performance requirements are quickly and accurately obtained under the condition that a large amount of early-stage chemical synthesis and experimental screening are not needed. According to the method, through deep combination of high-throughput prediction of machine learning and virtual experiment verification of molecular dynamics, efficient and automatic design of the interface modifier molecules is achieved, experimental synthesis and trial and error links are greatly reduced, the research and development efficiency is improved, and the method is widely suitable for design and optimization of the interface modifier molecules of the carbon fiber / resin-based composite material.