677 results about "Molecular dynamics" patented technology

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 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.

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 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 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.

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 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 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.

The invention discloses a multi-scale simulation method for a thermal compression deformation behavior of TC4 titanium alloy, and belongs to the field of metal material processing simulation. Comprising the following steps: 1, carrying out thermal compression experiments at different temperatures to obtain a deformed sample; 2, analyzing the grain orientation, orientation difference angle and texture evolution of the sample by using an electron back scattering diffraction technology; 3, constructing a crystal plasticity finite element model, and simulating a polycrystalline deformation process; 4, constructing a molecular dynamics model, and simulating phase change and dislocation evolution in thermal compression and cooling processes; and 5, based on a multi-scale simulation result, hot working parameters are optimized, dynamic recrystallization and a beta-to-alpha phase change path are regulated and controlled, and grain refinement and performance homogenization are achieved. The invention discloses a correlation mechanism of the microstructure and the mechanical property in TC4 titanium alloy thermal compression deformation, and provides a theoretical basis for optimization of a thermal processing technology of a high-performance titanium alloy component in the aerospace field.

A method of performing computation using a hybrid quantum-classical computing system comprising a classical computer and a quantum processor includes computing, by use of a classical computer, short-range inter-particle interaction energies and self-energies of a group of interacting particles, transforming the quantum processor from an initial state to a charge-position encoded state, applying Quantum Fourier transformation to the quantum processor, measuring an estimated amplitude of the Fourier transformed superposition state on the quantum processor, computing long-range inter-particle interaction energies based on the measured estimated amplitude of the Fourier transformed superposition state, and computing and outputting a sum of the short-range inter-particle interaction energies, the self-energies of the system, and the long-range inter-particle interaction energies as a total inter-particle interaction energies of the system.

The invention provides a polymer binder generation method and device, and the method comprises the steps: obtaining a polymer binder data set; inputting the molecular structure into a generation model to generate a polymer binder candidate structure; inputting the polymer binder candidate structure into a prediction model to predict a candidate parameter data set, the candidate parameter data set comprising candidate physical parameters, candidate chemical parameters and candidate performance parameters; performing molecular dynamics simulation according to the candidate structure, the candidate physical parameters, the candidate chemical parameters and the candidate performance parameters of the high-molecular binder, and determining performance evaluation indexes; and based on the performance evaluation index, optimizing the candidate structure of the high-molecular binder, and outputting the target high-molecular binder. According to the method and the device, the optimized target polymer binder is generated, the simulation cost is reduced, and the material performance is improved.

The invention relates to an intelligent design method of a radiation shielding material based on cross-scale error feedback lock step, which comprises the following steps: determining a material component set and acquiring an atomic-scale graph structure of the material component set, and extracting intrinsic performance parameters such as heat conductivity and attenuation coefficient through an atomic-scale prediction module; and then, constructing a design variable vector in combination with a multi-physical field condition, calculating equivalent performance parameters of the composite material, establishing a cross-scale lock step mechanism, and triggering a molecular dynamics and Monte Carlo recalculation module to carry out correction when the deviation between a predicted value and a mesoscopic recalculation result exceeds a limit. Based on the correction parameters, a candidate material scheme is generated by using a multi-objective optimization algorithm; and performing gamma-ray and neutron shielding performance simulation on the candidate schemes through a Monte Carlo radiation transfer model accelerated by a deep neural network, and comparing with an experimental result to realize self-adaptive material design driven by cross-scale errors. According to the method, the design precision and efficiency of the radiation shielding material are remarkably improved.

The invention discloses an alloy high-temperature and high-pressure environment simulation method under radiation action based on molecular dynamics, which comprises the following steps: establishing a molecular structure framework of an alloy according to a crystal structure and a lattice constant of the alloy, and setting initial parameters to construct an initial simulation model; performing relaxation operation on the molecular structure model in a high-temperature and high-pressure environment; performing irradiation damage simulation on the relaxed model based on molecular dynamics, selecting a central atom as a primary off-site atom, and setting incident energy and incident direction of the primary off-site atom to simulate atom cascade collision; keeping the incident direction unchanged, and adjusting the incident energy of the primary off-site atoms to obtain defect distribution and defect density under different energies; and analyzing the defect evolution law of the alloy in the high-temperature and high-pressure environment according to the simulation result. According to the method, the alloy material is observed from a microscopic atomic scale, so that a powerful support is provided for simulating a defect evolution process in a high-temperature and high-pressure environment and reflecting atomic collision of the alloy in a real radiation environment.

The invention discloses a molecular dynamics simulation method, device and equipment oriented to a molecular beam epitaxy process, and relates to the technical field of deposition in microelectronic manufacturing, and the method comprises the steps: analyzing a transition state path, and constructing an initialized data set; training an initial neural evolution potential function based on the initialized data set, performing a small-scale extrapolation experiment, establishing an evaluation function, screening a differentiation configuration, and executing DFT precision verification to form an enhanced training set; after multi-round iteration, a final potential function is output until a convergence condition is met; large-scale molecular dynamics simulation is carried out, different data sets are constructed, a potential function is trained after feature fusion and cross validation, deduction is carried out, the deposition rate is counted, and the reaction deposition selection ratio is estimated. According to the method, the dynamic evolution rule of the heterogeneous interface defect can be accurately analyzed, the real technological process problem is flexibly locked, and microsecond-level real-time closed-loop accurate control of the atomic layer growth rate is realized.

The invention relates to a molecular dynamics simulation method based on interaction of nucleic acid and protein, which comprises the following steps: carrying out molecular modeling on a target nucleic acid sequence to obtain a nucleic acid three-dimensional structure model; performing structure prediction on the target protein sequence according to the homologous template and a deep learning method to obtain a protein three-dimensional structure model; performing interaction site screening on the nucleic acid three-dimensional structure model and the protein three-dimensional structure model by using a molecular docking algorithm to obtain a compound initial conformation of nucleic acid and protein; based on multi-stage energy minimization and ensemble balance conditions, performing dynamic simulation on the initial conformation of the compound to obtain conformation trajectory data; and performing multi-dimensional analysis on the conformation trajectory data to obtain a quantitative index of the interaction between the nucleic acid and the protein. According to the method, the modeling precision is improved, manual intervention is reduced, and the simulation efficiency is improved.

The invention relates to the technical field of quality analysis of plastic packaging materials, and particularly discloses a quality analysis method of a plastic packaging product, which comprises the following steps of: acquiring chemical composition information of a target material by adopting Fourier transform infrared spectroscopy and thermogravimetric analysis, and constructing an atomic-scale three-dimensional molecular model on the basis of polymer types and auxiliary ratio; auxiliary agent diffusion behaviors are simulated and tracked through molecular dynamics, and key transmission parameters are obtained; importing the parameters into a finite element model, establishing a multi-physics field coupling migration simulation model, and simulating the migration trends of the assistants under different environmental conditions; establishing a quantitative correlation model to predict the long-term stability of the material by combining accelerated aging experiment data; according to the method, the prediction precision of the service life and the function stability of the plastic packaging material is improved, the research and development cost is reduced, and the product quality consistency and the market competitiveness are improved.

The invention provides a disease marker epitope prediction and antibody screening method, which belongs to the technical field of disease markers, and comprises the following steps: firstly, establishing a training data set containing a known antigen-antibody compound structure and disease tissue expression data; and obtaining target protein sequence information through liquid chromatography-mass spectrometry analysis and carrying out sequence comparison. And then a deep convolutional neural network is utilized to extract sequence features, and surface exposure sites are identified by combining secondary structure prediction and solvent accessibility analysis. After the features are integrated with sequence evolution conservative properties, a prediction model is constructed by using a random forest classifier. The method comprises the following steps: carrying out molecular dynamics simulation on a prediction result, screening first 10% of candidate sequences through a comprehensive scoring function and K-means clustering, and finally determining an antigen epitope sequence with the strongest binding activity through verification of an antigen chip and a fluorescence labeled antibody system, so that the technical problem that specific antigen epitopes are difficult to accurately predict and recognize in the prior art is solved.

The invention aims to provide a method, equipment, medium and program product for determining acidity coefficient, the method comprises the following steps: constructing a simulation box corresponding to protein, the simulation box comprising the protein, a corresponding solvent and a buffer solution; performing molecular dynamics simulation on the simulation box by utilizing a trained machine learning force field to obtain corresponding track file information; and determining an acidity coefficient corresponding to each drippable site in the protein based on the track file information. A dynamic process of protein in a solution is accurately and efficiently simulated by utilizing a machine learning force field, and an accurate acidity coefficient is calculated based on a state of a protein solution system in simulation.

The invention discloses a material performance prediction method and system based on deep learning, and relates to the technical field of material performance prediction, and the method comprises the steps: building a cross-level message passing network based on a double-layer graph data structure, extracting atomic-level and functional group-level feature embedding, and generating a molecular global latent variable through bidirectional cross-scale attention interaction; fusing molecular global latent variables with atomic-scale and functional group-scale features, applying physical constraints in combination with a chemical bonding rule base and a group contribution theory, and constructing a physical property prediction model; obtaining candidate molecules through autoregression generation and molecular force field verification based on a molecular global latent variable and a physical property prediction model; and screening candidate molecules by using the physical property prediction model, and outputting a final molecule set through molecular dynamics simulation and synthesis feasibility evaluation verification. Through multi-stage verification of molecular dynamics simulation and synthesis feasibility evaluation, a high-tension ring or a non-synthesized structure is effectively eliminated, so that the generated molecule has high performance and manufacturability.

The invention belongs to the technical field of ferrous metallurgy, and particularly relates to a method for determining dissolution kinetics of solute in metallurgical slag based on molecular dynamics simulation, compared with a traditional experimental method, molecular dynamics simulation does not need a large number of thermal state experiments and complex detection modes, and large-scale simulation calculation can be rapidly completed on a computer. And meanwhile, various different conditions can be simulated in a short time, and superior processes and slag systems can be quickly screened out. According to the method, the reaction which is difficult to observe clearly can be visualized, the atomic trajectory behavior of the dissolution behavior, the dynamic change, the dispersion process of the solute and the like are provided, rich and accurate information is provided for research on dissolution kinetics, and the dissolution rule and the structure evolution mechanism of the solute in the high-temperature melt can be revealed. In addition, the method is high in safety, and danger possibly brought by a high-temperature experiment is avoided.

The invention relates to an automatic generation method and system for a molecular dynamics simulation sampling strategy. The automatic generation method for the molecular dynamics simulation sampling strategy comprises the steps that molecular dynamics pre-simulation is conducted on a target system; collecting trajectory data of molecular dynamics pre-simulation; performing feature extraction based on the trajectory data to obtain trajectory features; screening out a key degree of freedom from the trajectory features by adopting an unsupervised model fusing a graph neural network and a self-attention mechanism; and based on the key degree of freedom, a molecular dynamics simulation sampling strategy is obtained through a PPO algorithm optimization strategy. The key degree of freedom is intelligently identified from the trajectory data, the adaptive molecular dynamics simulation sampling strategy is dynamically generated based on the key degree of freedom, collective variables do not need to be defined depending on expert experience, manual intervention is not needed, and the technical bottleneck of current molecular dynamics simulation can be broken through; and the exploration efficiency of the complex system conformation can be obviously improved.

The invention provides a thermal response hydrogel construction method based on solid-liquid interface repulsive force and water content, which comprises the following steps: establishing a coarse graining model of a thermal response polymer network and a water molecule system, and constructing a three-dimensional polymer gel network by adjusting the number of polymer chain monomers and the proportion of a cross-linking agent; constructing a simulation box according to the network geometric dimension and filling the simulation box with water beads; defining interaction parameters among beads under the dissipative particle dynamics model, performing molecular dynamics simulation, counting the mass center of a polymer skeleton and the number of water beads adjacent to an interface, and calculating the equilibrium moisture content; establishing a solid-liquid interface model, and calculating an equilibrium distance and interaction energy between a water bead plane and a polymer skeleton plane at the adsorption temperature; the network polymerization degree is optimized by adjusting the number of polymer chain monomers, and the optimal design of the thermal response desorption performance is achieved. The method provided by the invention is suitable for macroscopic hydrogel structure evaluation, can quickly obtain equilibrium moisture content and interfacial interaction energy, and guides reasonable proportioning and preparation of the hydrogel.

The invention discloses a Fe-Mn-C alloy interstitial atom potential function construction method based on a first principle and machine learning, and belongs to the technical field of material science and computational simulation. The construction method comprises the following steps: firstly, constructing a Fe-Mn-C alloy interstitial element atomic structure model, carrying out structure optimization on the interstitial element atomic structure model by utilizing a first principle, then carrying out first principle molecular dynamics (AIMD) multi-temperature sampling on the model after the structure optimization, and dividing a training set and a verification set; and finally, machine learning is conducted, and the Fe-Mn-C alloy gap potential function is output. According to the construction method, unification of high precision and high efficiency of the potential function is achieved, and the obtained Fe-Mn-C alloy gap potential function can be applied to research of a face-centered cubic (FCC) Fe-Mn-C alloy.

The invention relates to a method for constructing polyurethane molecular models with different crosslinking uniformity degrees, and belongs to the technical field of molecular dynamics simulation. According to the method, an initial amorphous structure is constructed through Packmol and Lammpps software, dynamic relaxation is carried out, a Python script is combined to simulate cross-linking reactions of isocyanate and polyol in different space lattices, the cross-linking cutoff distance and a reaction area are controlled, and regulation and control of the local cross-linking degree are achieved. According to the method, the average crosslinking degree and the crosslinking distribution uniformity coefficient can be calculated, the spatial non-uniformity of a crosslinking network can be quantitatively described, the method is suitable for researching the influence of factors such as high temperature, nano filler introduction or non-uniform mixing of a reaction system and the like on the structural performance of polyurethane, and the technical blank in the aspect of non-uniform crosslinking model construction is filled.