471 results about "Molecular dynamics" patented technology

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 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 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.

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 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 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 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 discloses an explanation method for damage threshold improvement of a fused quartz material in a femtosecond laser processing / welding process. By establishing a fused quartz electron-lattice heat transfer model under femtosecond laser irradiation, a dissociation model of environment molecules and interstitial molecules under the action of femtosecond laser and a defect-containing fused quartz molecule dynamic reaction model based on a ReaxFF reaction force field, atomic structure changes in a system before and after a reaction are analyzed; and finally, obtaining a chemical reaction mechanism of the fused quartz containing the defects under the action of the femtosecond laser, and explaining the phenomenon that the damage threshold of the fused quartz is increased after the fused quartz is irradiated by the low-flux femtosecond laser. According to the method, the research in the current fused quartz femtosecond laser processing / welding process is perfected, and meanwhile, the theoretical blank that the damage threshold lifting mechanism is not clear in the fused quartz glass femtosecond laser processing / welding process is filled up.

The invention provides a Kovar alloy nano-indentation and nano-friction simulation method based on molecular dynamics. The technical key points are as follows: carrying out initial setting on a simulation system and establishing a model; defining a potential function; performing model initialization, including energy minimization, temperature initialization and system relaxation; performing nanoindentation simulation and extracting data; performing nanometer friction simulation and extracting data; and processing and analyzing simulated data, namely outputting a depth-load curve, calculating hardness, fitting and calculating an elastic modulus, calculating a friction force and a friction coefficient, and analyzing wear morphology and dislocation evolution in the alloy through a simulation process file. By analyzing the mechanical properties and the frictional wear properties of the Kovar alloy at different environment temperatures, stable and reliable simulation data is obtained, the actual experiment cost is greatly reduced, and technical guidance is provided for application of the Kovar alloy in the field of vacuum sealing elements.

The invention relates to a method and equipment for predicting and optimizing CO2 mineralization storage efficiency and a storage medium. The method comprises the following steps: constructing a mineral supercell structure model and obtaining a stable configuration of a target mineral; constructing a vacuum layer based on the stable configuration, and filling H2O molecules and CO2 molecules to construct a CO2-water-mineral system model; carrying out reaction molecular dynamics simulation to obtain atomic motion trail and bond level change information; calculating the CO2 sequestration rate; drawing a three-dimensional carbon sequestration mechanism distribution diagram; and the sealing conditions of actual geological sealing engineering are optimized. According to the method, adsorption and chemical reaction behaviors of CO2 in underground rock pores are reduced, the storage condition of CO2 is quantified, then the influence of parameters such as temperature, pressure and water content on the CO2 storage rate is evaluated, the storage effects under different conditions are compared, a basis can be provided for optimizing the optimal storage condition, and the storage efficiency of CO2 is improved. The method has important guiding significance on design and optimization of a sealing scheme in practical engineering.

The invention relates to a protein pocket detection system and method and a storage medium, and the detection system comprises a conformation generation module, a pocket detection module, a task scheduling and management module and a visualization module: the task scheduling and management module obtains to-be-detected protein data; the conformation generation module is used for generating a conformation track of the protein data by utilizing molecular dynamics simulation based on the protein data acquired by the task scheduling and management module; the pocket detection module is used for determining pocket data based on the conformation track generated by the conformation generation module; wherein the pocket data is used for representing the stable data of the spatial position and the geometric shape of the protein data in the change process of the conformation trajectory; and the visualization module is used for rendering and outputting the conformation track generated by the conformation generation module and the pocket data determined by the pocket detection module. The problem that in the prior art, key drug targets generated in the dynamic process of protein cannot be efficiently and conveniently found is solved.

The invention relates to the field of oil and gas reservoir exploration and development, and discloses a shale oil reservoir carbon dioxide injection exploitation effect prediction method and device, and the method comprises the steps: obtaining the characteristic parameters of a target core and the engineering parameters of a target block; constructing molecular dynamics models corresponding to different minerals in the target rock core according to the characteristic parameters and the engineering parameters; determining a target relational expression according to a simulation result of each molecular dynamics model; based on a target pore network model and the target relational expression, the exploitation effect of carbon dioxide injection of the shale oil reservoir is determined, and the target pore network model is obtained based on the characteristic parameters. Due to the fact that the flow rule of fluid in the nanoscale pores, nanoscale effect changes and differences among different mineral types are considered when the exploitation effect of the shale oil reservoir injected with the carbon dioxide is predicted, the accuracy of prediction of the exploitation effect of the shale oil reservoir injected with the carbon dioxide can be improved.

The invention discloses a high polymer material performance prediction method and system based on deep learning, and the method comprises the steps: constructing an automatic encoder, and carrying out the dimension reduction of various data of a high polymer material through unsupervised learning; optimizing a multi-mode encoder structure by adopting an automatic design mechanism; extracting a fusion characteristic value of the high polymer material by using a multi-modal data encoder; dynamic attention fusion: introducing a dynamic gating weight to adaptively distribute modal weights to input data; introducing physical constraint, and embedding molecular dynamics into back propagation; performing quantum circuit acceleration graph convolution; and predicting and outputting, mapping the fusion characteristic value to the tensile strength and elastic modulus performance indexes of the high polymer material, and realizing nonlinear regression through a multi-layer perceptron. According to the method, a material molecular dynamics equation is converted into a forward propagation kernel from a posterior constraint, the dynamic behaviors of molecules can be simulated and predicted more accurately, and the calculation efficiency is improved while the precision is ensured.

The invention relates to the technical field of metal powder metallurgy process simulation. The high-entropy alloy hot pressing sintering and nanoindentation coupled molecular dynamics simulation method comprises the steps that a three-dimensional multi-particle atomic model of a high-entropy alloy is built, and initial boundary conditions are set; adjusting an initial boundary condition to be a contractility boundary condition, setting process parameters of hot pressing sintering on the model, and executing molecular dynamics simulation of the hot pressing sintering process; performing molecular dynamics and Monte Carlo hybrid simulation on the sintered model; mechanical boundary conditions are set for the optimized atomic model, a virtual indenter is controlled to be pressed into the optimized atomic model, molecular dynamics simulation of a nanoindentation test is executed, and physical field response parameters are collected in the test process; and based on the physical field response parameters, macroscopic mechanical property indexes are extracted. According to the method, the mapping relation between the hot pressing sintering process parameters and the macroscopic mechanical property is established, and a basis is provided for optimization of the hot pressing sintering process of the high-entropy alloy powder.

The invention discloses a liquid crystal performance prediction method and device based on molecular dynamics and a liquid crystal theory, and relates to the technical field of liquid crystal performance prediction.The method comprises the steps that the molecular structure of liquid crystal to be simulated is confirmed, and the molecular structure of the liquid crystal is obtained; generating a coarse graining molecular structure and a coarse graining force field parameter through a coarse graining scheme; constructing a nematic phase initial structure of the volume phase system; performing molecular dynamics simulation at a set temperature to obtain molecular dynamics simulation trajectory data, calculating to obtain a molecular director of liquid crystal molecules, and determining an overall director; a second-order-sequence parameter and a fourth-order-sequence parameter are obtained through calculation; constructing a correlation function and fitting to obtain the rotation relaxation time of the system; according to a Nemtsov-Zakharov formula or a Fialkowski formula, the rotary viscosity of the liquid crystal is obtained through calculation; constructing a change relation of the second-order-sequence parameter along with the temperature; and performing fitting analysis through a Maier-Saupe theory or a critical index formula to determine a phase change point. The liquid crystal performance prediction efficiency can be improved, the cost is reduced, and the prediction precision is improved.

The invention relates to a chemical reaction path prediction method and system. The method comprises the following steps: defining a reaction pair containing reactants and potential products; performing combined optimization treatment on the reactants and the potential products by adopting a molecular dynamics method and a quantum chemistry method to generate an initial reactant configuration and an initial product configuration; based on the aligned initial reactant configuration and initial product configuration, determining initial reaction path information connecting the initial reactant configuration and the initial product configuration by adopting a quantum chemistry method; the invention provides a chemical reaction path prediction method and a chemical reaction path prediction system using the method, which are used for positioning and verifying accurate transition states of reactants and potential products based on initial reaction path information by adopting a quantum chemistry method, and can improve the initial guess quality for high-precision calculation and improve the accuracy of the chemical reaction path prediction. Therefore, the convergence success rate and accuracy of the transition state search are greatly improved, and the overall calculation cost is further reduced on the premise of ensuring the prediction precision.

The invention discloses a method and system for simulating the operating environment of a hydrogen energy reaction kettle, and the method comprises the steps: building a multi-scale simulation frame based on the hydrogen energy reaction kettle, and carrying out the modeling of a micro scale, a mesoscale and a macro scale of the hydrogen energy reaction kettle; performing multi-physical field coupling simulation on the hydrogen energy reaction kettle, including coupling calculation of a flow field, a temperature field, a concentration field and a stress field in an operation environment of the hydrogen energy reaction kettle; performing intelligent optimization simulation based on a multi-scale simulation framework and multi-physics field coupling simulation, wherein the intelligent optimization simulation comprises adaptive grid optimization, non-uniform inlet simulation and two-phase flow simulation; and constructing a digital twin platform, and carrying out system-level dynamic simulation and intelligent control on the hydrogen energy reaction kettle based on the digital twin platform. According to the method, all-around modeling and simulation from a micro scale to a macro scale are realized through a plurality of methods such as a density functional theory, molecular dynamics, a lattice Boltzmann method and computational fluid dynamics.

The invention provides a coal surface wettability prediction method based on a molecular dynamics simulation technology, and the method comprises the steps: constructing a coal macromolecular model, carrying out geometric structure optimization and annealing dynamics simulation, carrying out the section processing of the model, introducing a vacuum layer, and constructing a coal surface model with a preset density; performing isothermal adsorption simulation on the coal surface model, constructing a water ball model with a preset diameter, placing the water ball model on the coal surface after gas adsorption to form a coal-gas-water three-phase composite model, performing geometric structure optimization treatment on the coal-gas-water three-phase composite model, and selecting the model with the lowest energy after optimization; performing NVT ensemble molecular dynamics simulation on the coal-gas-water three-phase composite model to obtain a movement track of a water ball on the coal surface after the system is balanced; and selecting the last frame of balanced snapshot from the motion trail for analysis, selecting the vicinity of the contact point of the water polo and the base in the snapshot, making a tangent line along the contour of the water polo, and measuring the included angle between the tangent line and the base line.

The invention discloses a multi-scale modeling and simulation prediction method and device for a two-phase composite material, and the method comprises the following steps: S1, carrying out the molecular dynamics simulation of the penetration process of a solution in the composite material based on molecular dynamics, extracting the microscopic information in the simulation process, and obtaining a microstructure model of the composite material; s2, mapping the microstructure model to a finite element grid, and reserving structures between particles and interfaces to obtain a finite element model; s3, inserting a cohesive zone unit into an interface of the finite element model, and modeling an interface adhesion and separation phenomenon between a matrix and particles of the composite material based on the cohesive zone unit; and S4, carrying out analogue simulation on the whole damage evolution process based on the finite element model to realize mechanical simulation analysis and prediction. According to the method, composite material modeling is carried out based on a real microstructure, and more accurate mechanical simulation analysis is carried out based on the modeling.

The invention discloses an interfacial tension prediction method and device based on machine learning and molecular dynamics, and relates to the technical field of molecular dynamics simulation. The method comprises the following steps: constructing a mixed system model comprising CO2, surfactant molecules and crude oil, defining related parameters of a surfactant as a variable set, simulating by adopting molecular dynamics software, acquiring simulation data and interfacial tension data, and preprocessing; and taking the temperature, the pressure and the variable set as input characteristics, taking the interfacial tension as a target variable, and training a LightGBM model to obtain a prediction model. Each particle represents the configuration of one surfactant, the prediction model is used for obtaining the predicted interfacial tension corresponding to each particle, the speed and position of the particles are iteratively updated, and the optimal configuration of the surfactant for minimizing the interfacial tension is obtained. The problem of low oil exploitation efficiency caused by multiple exploitation operations due to poor oil displacement effect caused by incapability of accurately predicting the interfacial tension is solved.

The application provides a kind of ionic liquid electric spray micro force calculation method, device and electronic equipment, it is related to data processing field, the method is carried out original molecular dynamics simulation in the negative gradient calculation of position to atomic potential energy and obtains the force model, the micro force decoupling of ionic liquid electric spray under molecular dynamics simulation is executed;Based on the micro force model, the molecular dynamics trajectory data is recalculated by modifying the molecular dynamics simulation code parameters, the pre-data of different micro forces is obtained, and the atomic force calculation result is output based on the pre-data of different micro forces;The atomic force calculation result corresponding to the steady state part of system is calculated by time and space average, and the spatial distribution calculation result of micro force and electric field is obtained;After a spatially uniform distribution of constant electric field is applied to the system, the first external electric field received by the charged atom is calculated using the above scheme, and the applied electric field and the calculated electric field are consistent, which verifies that the scheme is executed without error.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a generative machine learning model In one aspect, a method comprises: obtaining a set of training examples for training a generative machine learning model to perform a structure prediction task; augmenting the set of training examples to include a plurality of new training examples, comprising, for each training example in the set of training examples: performing a molecular dynamics simulation of a molecular system, starting from a conformation of the molecular system that is specified by the training example, to generate a trajectory of new conformations adopted by the molecular system over a duration of time; generating one or more new training examples corresponding to the molecular system; and training the generative machine learning model to perform the structure prediction task on the augmented set of training examples.

The invention discloses a foamed rubber asphalt formula determination method. The method comprises the following steps: S1, constructing a molecular model of a foamed rubber asphalt composite system; s2, performing molecular dynamics simulation on the molecular model to obtain a balanced stable structure configuration; s3, based on the stable structure configuration, under the conditions of different rubber mixing amounts, foaming medium mixing amounts and temperatures, calculating the shear modulus, interface adhesion work and fracture energy of the foam rubber asphalt system; and S4, performing multi-objective optimization by taking the shear modulus, the interface adhesion work and the fracture energy as performance indexes, and determining a combination of the temperature, the foaming medium mixing amount and the rubber mixing amount of the optimal formula of the foamed rubber asphalt. According to the method, the combination of the foaming temperature, the foaming medium mixing amount and the rubber mixing amount is determined through molecular modeling and a multi-objective optimization algorithm, and performance prediction and parameter optimization of the foamed rubber asphalt are achieved.

The invention discloses a method for analyzing the dynamic evolution process of a methane leakage nano-channel in cold spring hydrate, and belongs to the field of natural gas hydrate science and engineering. The method comprises the following steps: constructing a hydrate molecular model and selecting a methane and water molecule force field; a hydrate block and a middle gas channel are arranged in the simulation box, and the gas channel is filled with a methane and water mixture to construct an initial simulation system; performing molecular dynamics simulation on the system under the conditions of energy minimization, pre-balance and different pressures and methane accelerations, and remodeling a gas channel dynamic evolution process in a hydrate growth process; carrying out analysis and three-dimensional rendering on the simulated track, and drawing a three-dimensional morphology graph of the gas channel changing along with time; quantitatively extracting surface area data of the gas channel, and calculating key kinetic parameters, including attenuation rate and half-life period, of the evolution process of the gas channel. According to the invention, the quantitative characterization of the dynamic evolution process of the methane leakage nano-channel in the cold spring environment is systematically realized on the molecular scale for the first time, and the contraction closing behavior of the gas channel under the condition of no methane leakage and the dynamic maintaining or adjusting process of the gas channel under the condition of continuous seepage can be accurately simulated and distinguished; the limitation of a traditional static pore model is overcome, and key basic parameters and method support are provided for revealing the interaction mechanism of methane migration and hydrate growth and constructing a cross-scale seepage model.

The invention discloses a SiC wafer surface oxidation behavior simulation method, system and device, and relates to the technical field of molecular dynamics simulation, and the method comprises the following steps: initializing a plurality of roughness related parameters in MATLAB, and setting simulation box sizes of a Si surface initial model and a C surface initial model; reading atomic positions in the Si surface initial model and the C surface initial model; circulation is carried out on different roughness related parameters, and a corresponding rough surface is generated in each circulation; cutting atomic positions based on different rough surfaces to obtain a plurality of Si surface roughness models and C surface roughness models, and inputting the Si surface roughness models and the C surface roughness models to LAMMPS; and performing OH deposition on the Si surface roughness models and the C surface roughness models in the LAMMPS, and simulating the surface oxidation process. According to the method, by introducing the random roughness, the rough morphology of the SiC surface is accurately simulated, and the authenticity and practicability of research are remarkably improved.