154 results about "Molecular simulation" patented technology

The invention relates to the field of pesticide composition screening, and discloses a stabilizer screening process for improving the low-temperature stability of a chlorantraniliprole and lufenuron suspending agent, which comprises the following steps: a) analyzing the low-temperature behavior of chlorantraniliprole and lufenuron based on molecular simulation, constructing a candidate stabilizer molecule library, and preliminarily screening candidate stabilizers; b) applying low-temperature stress to the chlorantraniliprole and lufenuron suspending agent sample containing the candidate stabilizer by using a microfluidic platform, and performing in-situ dynamic characterization to obtain dynamic data related to the low-temperature stability of the sample; and c) processing the dynamic data, and establishing an evaluation index representing the low-temperature stability performance of the candidate stabilizer. According to the invention, a cognitive bridge from a micromechanism to a mesoscopic phenomenon is constructed by integrating molecular simulation pre-screening and microfluidic high-throughput dynamic characterization. The problems of long research and development cycle and resource consumption caused by a large number of trial and error and experience judgment in a traditional screening method are effectively solved.

The invention discloses a molecular simulation method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring molecular system information of a current molecular configuration; calculating the first energy and the first gradient of the key atom region by adopting a variable component sub-feature solicitation solver algorithm, and calculating the second energy and the second gradient of the non-key atom region; wherein the first gradient is used for expressing the stress condition of each atom in the key atom area; the second gradient is used for expressing the stress condition of each atom in the non-key atom area; and updating the current molecular configuration according to the first energy, the first gradient, the second energy and the second gradient to obtain a target molecular configuration. According to the method and the device, the technical problem that the accuracy of the simulation result cannot be ensured under the condition that the computing resources are limited due to excessive computing resources consumed by the quantum chemical computing mode when the molecules are simulated by adopting the quantum chemical computing mode in the related technology is solved.

The invention provides a method for identifying a microscopic association body structure in a heavy oil molecule simulation trajectory, which comprises the following steps of: analyzing a molecular dynamics trajectory, identifying an aromatic ring structure in a heavy oil molecule component, calculating a geometric center of the aromatic ring structure, combining a periodic boundary condition, and applying an improved DBSCAN clustering algorithm to realize automatic identification and clustering of an association body; and performing post-processing optimization on the clustering result, merging the shared clusters to generate a final association body set, and analyzing the number, size distribution, morphological characteristics and dynamic evolution information of the association bodies based on the structural characteristics of the final association body set. The method can accurately capture pi-pi accumulation behaviors of heavy oil molecules, further reveals morphological characteristics and dynamic evolution laws of heavy oil microassociates and influences of the heavy oil microassociates on the apparent viscosity of heavy oil, provides theoretical support for heavy oil viscosity-causing mechanism analysis and viscosity reducer design, can complement experimental research, and has a wide application prospect. And efficient development and green utilization of a complex thickened oil system are promoted.

The invention relates to the technical field of machine learning potential molecular simulation, and provides a montmorillonite thermal conductivity prediction method based on NEP machine learning potential, and the method comprises the following steps: constructing a montmorillonite crystal structure model, carrying out first principle molecular dynamics calculation on the montmorillonite crystal structure model, introducing lattice distortion and atomic disturbance, and generating an initial structure set; performing static single-point DFT calculation on the initial structure set, and constructing a training data set; based on an NEP machine learning framework, utilizing the training data set to train and generate a potential function; optimizing the generation process of the potential function by adopting an active learning strategy; and performing thermal conductivity prediction verification on the potential function, and obtaining a thermal conductivity prediction result of the montmorillonite based on the potential function passing the verification. The defect that a traditional classical potential function depends on an empirical or semi-empirical model and is limited in precision and applicability is overcome, the problem that the dynamic behavior and the heat conduction mechanism of a complex material are difficult to accurately capture is solved, the calculation cost is reduced, and meanwhile the precision is kept.

The invention relates to the technical field of molecular simulation, and provides a method for constructing a heavy component average molecular structure of thickened oil, which comprises the following steps: inputting parameters, generating a core aromatic ring unit, constructing an inner-layer aliphatic ring unit, constructing an outer-layer aliphatic ring unit, generating a side chain unit, and storing and outputting structural information. A plurality of molecular structures meeting experimental data are generated at a time through a traversal algorithm thought, composition characteristics of a heavy oil complex mixture are met, more available molecular structures are provided for molecular simulation, the accuracy and efficiency of molecular modeling are improved, the molecular structures completely meeting the experimental data can be rapidly generated, and the method is suitable for large-scale popularization and application. And only the mass spectrometer, the elemental analyzer, the nuclear magnetic resonance hydrogen spectrum and the infrared spectrum data need to be input, so that the experiment cost is reduced.

The invention relates to the technical field of molecular simulation, and discloses a molecular simulation analysis method of a functionalized covalent organic framework confinement ionic liquid microstructure, and the method specifically comprises the following steps: converting force field files of COF and anions and cations of ionic liquid into coordinate files by using a Python tool; the anions and the cations are confined to a pore channel formed by COF stacking to form a composite membrane, and the whole system is placed in a simulation box; data and input files are generated according to the potential file, and LAMMPS software is used for conducting molecular dynamics simulation on a set simulation box; the two-dimensional density, cation orientation and the like of the ionic liquid are analyzed through a Python post-treatment molecular dynamics track, and meanwhile the porosity and the free volume of the COF are calculated. According to the method, a regulation and control mechanism of the COF confinement ionic liquid can be obtained, and a theoretical basis is provided for design and preparation of a COF confinement ionic liquid membrane material.

The invention provides a molecular simulation method and device for interaction behaviors of vulcanized rubber powder and asphalt, and relates to the technical field of molecular simulation, and the method comprises the following steps: respectively constructing a rubber powder molecular model and an asphalt molecular model through molecular simulation software; establishing a rubber powder modified asphalt system model according to the rubber powder molecular model and the asphalt molecular model; distributing molecular force fields to the rubber powder molecular model, the asphalt molecular model and the rubber powder modified asphalt system model; performing energy minimization processing and relaxation operation on the rubber powder modified asphalt system model, and calculating thermodynamic parameters and particle motion trails of the rubber powder modified asphalt system model; and evaluating the interaction effect of the vulcanized rubber powder and the asphalt according to the thermodynamic parameters and the particle motion trail of the rubber powder modified asphalt system model. According to the molecular simulation method, the interaction between the vulcanized rubber powder and the asphalt is systematically explained from the molecular level.

The invention discloses an interactive catalysis method and platform based on molecular simulation and artificial intelligence. According to the method, intelligent design of a catalyst is achieved through data classification, condition optimization, structure modeling and mechanism analysis. Catalytic data uploaded by a user is subjected to standardization processing and then is divided into two types: a data set A is used for recommending an optimal reaction condition by a multi-objective optimization algorithm; a three-dimensional model is constructed after a catalyst structure of the data set B is optimized through quantum chemistry software, a plurality of intermediates and transition state structures are optimized, and geometric / electronic descriptors are calculated. And constructing a dual-model collaborative prediction system of the model E and the model F through a feature screening and dimension reduction technology, and finally screening the high-performance catalyst by utilizing a model F driven molecule generation algorithm. According to the method, flexible adaptation to specific application scenes can be achieved, the urgent requirements of the industry for efficient and accurate catalyst performance prediction and intelligent design are met, cost reduction and efficiency improvement of the industry are effectively assisted, and the research and development process of the catalyst is accelerated.

The invention discloses an interface thickness parameter acquisition method based on a microcosmic full-atom composite model, and belongs to the field of molecular simulation. The method comprises the following steps of: firstly, constructing a periodic model of a polymer A and a periodic model of a polymer B by utilizing MS software, and after the periodic models are combined, obtaining a global energy minimization total-atom composite model through geometric optimization and dynamic equilibrium optimization; finally, atomic concentration distribution data points of the full-atomic composite model are calculated in an MS software Perl script module through the secondary programming technology, an atomic concentration distribution curve is drawn, and smooth processing is conducted; setting a judgment benchmark of the interface thickness of the full-atom composite model, and judging whether the interface thickness exists in the full-atom composite model or not; and defining an interface thickness calculation formula of the full-atom composite model, and calculating the interface thickness of the full-atom composite model with the interface thickness, so as to quickly, efficiently, accurately and quantitatively calculate the thickness result of the interface layer of the polymer composite material. In addition, the method has the advantages of being convenient to operate and the like.

The invention discloses a molecular simulation-based method for estimating CO2 / CH4 adsorption capacity in a shale reservoir, which comprises the following steps of: firstly, selecting different mineral components from the shale reservoir, and determining reservoir temperature and pressure corresponding to each mineral component; the method comprises the following steps: constructing molecular models of different mineral components in a shale reservoir and a to-be-detected gas molecular model by using molecular simulation software, and carrying out geometric optimization; setting temperature and pressure conditions conforming to the actual reservoir in the simulation environment, and introducing gas molecules to perform molecular dynamics simulation so as to research the competitive adsorption behavior and obtain adsorption quantity data; based on simulation data, an adsorption capacity calculation expression comprehensively considering the coupling influence of multiple factors such as temperature and pressure is established, and the actual shale reservoir gas adsorption capacity is rapidly and accurately estimated. Through molecular simulation, the limitation that a traditional experiment is long in period, high in cost and difficult to separate multi-factor influences is overcome, and an efficient and reliable theoretical tool is provided for shale gas reserve prediction and carbon dioxide geological storage benefit evaluation.

The invention relates to the technical field of safe conveying of oil and gas pipelines, in particular to a polypeptide type hydrate kinetic inhibitor screening method based on molecular simulation, which comprises the following steps: selecting target protein related to hydrate growth, and designing a polypeptide sequence; performing polypeptide pre-screening; evaluating the binding affinity and binding mode of the polypeptide and the target protein; performing MD simulation on the screened high-affinity polypeptide; verifying the binding affinity of the polypeptide and the target protein; and collecting all experiment and simulation data for statistical analysis, and optimizing the polypeptide sequence. The structure and function of the target protein are systematically analyzed by using a bioinformatics database and tools, it is ensured that the selected target plays an important role in hydrate formation, and meanwhile, a polypeptide sequence designed based on the target protein structure can effectively improve the binding capacity; and in the pre-screening and docking analysis process of the polypeptide, dynamic parameters are simulated and extracted by molecular dynamics, so that the interaction between the polypeptide and the target protein can be deeply understood.

The invention discloses a method for screening and extracting DES of tea saponin based on molecular simulation and machine learning, and relates to the technical field of tea saponin extraction.The method comprises the steps that tea saponin, cellulose, a hydrogen bond donor, a hydrogen bond receptor and a DES molecular model are constructed and optimized, and a molecular polarity index is obtained; the method comprises the following steps: generating a topology, obtaining a trajectory and energy through MD simulation, and calculating E (TeaS-DES), E (TeaS-Cell) and SI; and measuring the extraction rate, combining the molecular polarity index, SI, process parameters and the like to construct a data set to train a machine learning model, predicting the extraction rate, and locking the optimal DES and process. According to the method, DES screening can be directly guided through simulated energy and structure descriptors, the number of experiments is reduced, and the research and development period is shortened.

The present invention efficiently calculates molecular energy corresponding to a plurality of interatomic distances.　An information processing device (10) estimates, for interatomic distances (14a, 14b, 14c), estimated execution times (15a, 15b, 15c) and estimated repetition counts (16a, 16b, 16c) of an algorithm that calculates molecular energies using quantum circuit data, on the basis of molecular information (13). The information processing device (10) determines repetition counts (18a, 18b, 18c) for the interatomic distances (14a, 14b, 14c) on the basis of a restriction time (17), the estimated execution times (15a, 15b, 15c), and the estimated repetition counts (16a, 16b, 16c). The information processing device (10) calculates, by executing the algorithm, molecular energies (19a, 19b, 19c) for the interatomic distances (14a, 14b, 14c) on the basis of the repetition counts (18a, 18b, 18c).

The invention belongs to the field of computational material science, artificial intelligence algorithm and nano composite material performance prediction, and particularly relates to a conductivity prediction system and method based on three-dimensional voxelization representation and a graph neural network, and the system comprises a voxelization module, a graph construction module, a graph neural network prediction module and an interpretability analysis module which are sequentially connected according to a process. The voxelization module converts three-dimensional molecular simulation structure data into a discrete local density field; the graph construction module generates a voxel graph structure; the graph neural network prediction module adopts an end-to-end design, is composed of a cascade convolution attention layer and the like through incremental feature learning, and outputs a conductivity prediction result; the interpretability analysis module reveals a structure-performance relationship. The method comprises the steps of data acquisition, voxelization processing, voxel graph structure construction and input model prediction and analysis. According to the method, the conductivity of the nanocomposite can be efficiently and accurately predicted, and powerful support is provided for material design and optimization.

Provided is a system for predicting compatibility between different kinds of resin materials, the method being capable of predicting compatibility such as mutual solubility and interfacial adhesion between constituent materials in a blend material prepared by mixing a plurality of resin materials. The system is characterized by comprising: a three-dimensional molecular model creation unit that creates a three-dimensional molecular model of each of a plurality of resin materials using, as input information, information obtained by digitizing the chemical structures of the resin materials; a molecular entanglement model creation unit that, using the three-dimensional molecular model, temperature, and pressure as input information, creates a molecular entanglement model of different kinds of resin materials in which two kinds of the three-dimensional molecular models of each of the resin materials are combined; a mutual diffusion coefficient calculation unit that executes a molecular simulation on the basis of the molecular entanglement model and calculates a mutual diffusion coefficient between the different kinds of resin materials; a fracture energy calculation unit that performs a molecular simulation on the basis of the molecular entanglement model and calculates a fracture energy; a trajectory saving unit in which the time evolution of the coordinates of the molecular entanglement model in the process of calculating the fracture energy is saved; and a simulation result output unit that outputs the mutual diffusion coefficient and the fracture energy as numerical information and outputs the time evolution of the coordinates as a moving image.

The invention discloses an ACE (angiotensin converting enzyme) inhibitory peptide with antihypertensive activity and application thereof, and belongs to the technical field of active peptide biology, amino acid sequences of the ACE inhibitory peptide are EYPVK and SYPVK, the two peptides are obtained through module replacement design, the ACE inhibitory activity of the two peptides is remarkably improved compared with that of an original peptide LYPVK, IC50 values are respectively reduced to 14.82 + / -0.82 mu M and 12.83 + / -0.79 mu M from 117.14 + / -1.40 mu M, and the inhibitory activity is improved by 13.5% and 25.1%. Molecular simulation analysis shows that the interaction force between the two and ACE is stronger, and a potential modular effect exists between sequences. Besides, the polypeptide has remarkable blood pressure lowering activity on human umbilical vein endothelial cells, plays a role by regulating release of NO and ET-1, has the activity superior to that of original peptides, has the advantages of good digestive enzyme stability, small molecular weight and good water solubility, and is suitable for developing related products for lowering blood pressure.

The invention discloses a method for analyzing a fiber and resin interface area based on molecular simulation, and belongs to the field of building a fiber and resin interface model based on a molecular simulation technology, and carrying out geometric optimization and dynamic balance on the interface model; counting and presenting the fiber surface conformation of the interface model; constructing a model of independent resin molecular chains and fibers, and explaining reasons of conformational changes of the resin molecular chains; based on the radial distribution function and the mean square displacement curve, analyzing the interface structure of the interface model and the activity of molecules; and constructing an interface model for interface nanoindentation simulation, and analyzing the stress bearing capacity of different areas of the interface and the motion displacement condition of atoms. According to the method, conformation and performance analysis of the fiber and resin interface are realized, favorable support can be provided for design, optimization and performance prediction of the fiber reinforced composite material, and the method has relatively good practicability.

This invention relates to a method for constructing a machine learning potential energy model based on hierarchical active learning, comprising the following steps: constructing a database of background solvent molecules and lithium salt molecules; initializing a machine learning potential energy model committee; outer active learning automatically exploring the chemical species composition space of the electrolyte, generating electrolyte formulations and initial structures; determining whether a structure needs to be included in the labeling range based on uncertainty calculations; automatically constructing a liquid phase environment based on classical force field simulation methods; performing high-precision simulations based on first-principles calculations and labeling the energy and force values ​​of the structures; inner active learning driving the machine learning potential energy model to explore the configuration space and label the energy and force values ​​of structures with high uncertainty; obtaining a first-principles structure database of the real liquid phase environment through several iterations; and training to obtain the final high-performance machine learning potential energy model. This invention systematically and comprehensively samples the chemical species composition space of the electrolyte and the molecular simulation configuration space based on hierarchical active learning, thereby ensuring the predictive performance and simulation accuracy of the machine learning potential model on unknown and complex electrolyte systems.

The application discloses a method for predicting polypeptides based on machine learning, comprising: predicting the binding site of PEX5 of the receptor; constructing a polypeptide sequence and determining the initial state of the polypeptide; generating the space required for polypeptide and PEX5 binding site collision simulation; performing polypeptide and PEX5 binding site collision simulation; performing data preprocessing; learning the known classified polypeptides through sklearn and outputting the combination of neural networks with high accuracy; reconstructing the neural network from sklearn through the neural network of Pytorch, learning the known classified polypeptides, predicting the unknown classified polypeptides, and outputting the confidence. The application can make accurate prediction of polypeptides by using various computer simulation methods such as molecular simulation and combining machine learning, can sufficiently reduce the order of magnitude of wet experiments, and thus reduce the cost and improve the efficiency. The method can also be used for screening the interaction of other functional polypeptides, drug polypeptides and corresponding target points.

The invention discloses a molecular dynamics simulation method for electron-induced gas desorption, and belongs to the technical field of molecular simulation. The method comprises the following steps: firstly, constructing an aluminum oxide molecular model, and then simulating adsorption equilibrium of hydrogen on the surface of aluminum oxide at different temperatures and air pressures by adopting a giant regular Monte Carlo method to obtain a stable adsorption configuration; then, based on the configuration, an electron force field is adopted to introduce explicit electron particles to simulate electron beam bombardment, and an electron-induced gas desorption kinetic process is simulated under a molecular dynamics framework; and finally, performing quantitative analysis on the desorption product. Through full-atom molecular dynamics simulation, coherent research from gas adsorption to electron-induced desorption is realized, the limitation that related experimental measurement is difficult and a micromechanism is difficult to reveal under a high-voltage condition is overcome, and an effective simulation tool is provided for deeply understanding a physical mechanism of dielectric surface gas desorption-induced discharge.

This invention provides a molecular simulation method for the pre-oxidation process of polyacrylonitrile fibers coupled with electron beam irradiation and heating, comprising the following steps: establishing a trans-syndiotactic stereotype model of polyacrylonitrile under a specific oxygen environment using computer molecular materials simulation software; inserting a temperature controller and creating a primary collision atom bombardment molecular system during the gradient heating process of polyacrylonitrile pre-oxidation using molecular dynamics methods to simulate the coupling effect of irradiation and heating; continuing the gradient heating to complete the molecular dynamics simulation of pre-oxidation; and outputting the microscopic results of the simulation process. This invention uses molecular dynamics methods to introduce electron beam irradiation during the gradient heating process of polyacrylonitrile to simulate the coupling effect of electron beam irradiation and heating in the pre-oxidation process of polyacrylonitrile. It proposes a set of controllable simulation parameters for the timing of electron beam irradiation introduction during the pre-oxidation process, which has guiding significance for exploring the pre-oxidation reaction mechanism of the coupling effect of electron beam irradiation and heating on polyacrylonitrile fibers.

This application discloses a method, equipment, medium, and product for detecting the content of silicone rubber, relating to the field of silicone rubber content detection. The method includes obtaining silicone rubber samples with different silicone rubber formulations; performing terahertz detection on the silicone rubber samples to obtain the corresponding terahertz signals; determining the corresponding absorbance based on the terahertz signals; performing molecular simulations on the main components of the silicone rubber samples to obtain the vibrational characteristic frequencies of the main components at different terahertz frequencies; determining a regression function for silicone rubber content based on the vibrational characteristic frequencies of the main components at different terahertz frequencies and the corresponding absorbances; obtaining the absorbance of the silicone rubber to be tested, and using the regression function to determine the silicone rubber content. This application can improve the accuracy and efficiency of silicone rubber content detection.

The invention relates to the technical field of shale reservoir mechanical property evaluation, and provides a shale mechanical property determination method and device based on molecular simulation. The method comprises the following steps: constructing a first slit hole model of minerals of a target shale sample, a second slit hole model of kerogen organic matters and a third slit hole model between different minerals and / or kerogen organic matters; adding molecules of different fluids, and constructing a first slit hole model, a second slit hole model and a third slit hole model containing different fluids; and constructing a mechanical property model of the target shale sample according to the mineral combination of the target shale sample, the first crystal orientation, the characteristics of different fluids, and the first slit hole model, the second slit hole model and the third slit hole model containing different fluids. The method solves the problem that an existing method is difficult to accurately quantify the mechanical effect under the microcosmic nanoscale fluid characteristic-mineral component coupling effect.

The application discloses a rubber-cement composite material surface modifier optimization method based on molecular simulation, which comprises the following steps: based on molecular dynamics simulation, a full-atom numerical model for characterizing the physicochemical properties of cement matrix, rubber and modifier material is constructed by using Materials Studio software; based on the full-atom numerical model, the Gibbs free energy of the material system is calculated by using a quantum mechanics method to predict whether chemical reaction or physical adsorption behavior occurs between the selected modifier and the two types of materials of rubber and cement, and finally a rubber-cement-based composite material system model before and after the treatment of the modifier is constructed; the free volume fraction and atomic concentration of the composite material system model before and after the modification are analyzed on a microscale to quantitatively evaluate the effect of the modifier on the material performance, and then the optimal surface modifier is determined. The application can construct a numerical model accurately reflecting the material properties and analyze and optimize the material surface / interface modifier.

The invention relates to a compound having antibacterial and antifungal properties and the chemical structure of Formula X, and to the synthesis method of said compound. Said compound is a piperazine-substituted quinone compound, and the position and orientation of the molecule within DNA and E. coli DNA gyrase B have been determined by means of molecular simulation.

The application discloses a kind of cross-scale multi-component deep shale gas mobility evaluation method, it is related to oil and gas resource evaluation technical field.The application utilizes molecular simulation software to construct multi-component deep shale gas pore occurrence model, utilizes its simulation to obtain the density variation curve of each component of shale gas fluid under different pressure conditions, divides adsorption zone and free zone, then determines the shale gas density calculation function of adsorption zone and free zone, establishes shale gas mobility evaluation formula under molecular scale and is applied to pore scale, in combination with the pore network model extracted from the core of deep shale reservoir according to field acquisition, obtains the size distribution of its throat and pore, respectively calculates adsorption zone volume and free zone volume, utilizes shale gas mobility evaluation formula to evaluate the shale gas operation of deep shale reservoir, realizes accurate evaluation to multi-component deep shale gas mobility on molecular level, provides technical support for the efficient formulation of deep shale gas development plan.

The invention discloses a calculation method of a rare earth oxide molecule simulation force field and a glass component modulus performance evaluation method, and the method comprises the steps: collecting parameters in a crystal database in a rare earth oxide crystal state, including lattice constants, space group numbers, stiffness matrix coefficients and density; establishing a potential energy relation model between rare earth oxide cations and oxygen atoms; fitting parameters of the potential energy relation model; constructing a rare earth oxide molecule simulation force field; collecting glass components and modulus performance data in the glass performance database; and calculating the modulus performance of the glass component by applying the rare earth oxide molecule simulation force field obtained by the calculation method of the rare earth oxide molecule simulation force field and combining a molecular dynamics simulation technology. According to the fitting method provided by the invention, the molecular simulation force field for accurately simulating the rare earth oxide can be obtained, and the Young modulus of the fiber glass containing the rare earth oxide is favorably simulated and calculated.

This invention discloses umami peptides derived from Litopenaeus vannamei and their applications, belonging to the field of bioactive peptide technology. Seven umami peptides are described, with amino acid sequences as follows: ASRM, RCNG, DNCY, GAEF, DEGF, EDMQF, and PNRMPY, as shown in SEQ ID NO. 1–7. The invention also discusses the application of these umami peptides as or in the preparation of umami enhancers. This invention utilizes virtual screening and molecular simulation techniques to discover new umami peptides from the protein sequences of Litopenaeus vannamei, enriching the marine umami peptide library. Furthermore, it investigates the key amino acids and mechanisms of action of umami peptide receptor binding, providing a reference for studying the umami presentation mechanisms of peptides with different structures.

The application discloses a method and device for determining gas storage efficiency of a hydrate promoter, the method comprising: constructing a gas-liquid two-phase molecular simulation system, the molecular simulation system comprising a filled gas box and a water solution box spliced in sequence along a set direction; inputting a plurality of test parameter groups into the gas-liquid two-phase molecular simulation system to form parameter regulation under a multivariate grid simulation framework, and realizing simulation of hydrate nucleation or growth of a gas; obtaining trajectory data of molecular dynamics simulation in the process of simulation of hydrate nucleation or growth of the gas, the trajectory data comprising the number of newly formed hydrate cages, the mass of the gas, the promoter and water in the hydrate within a set sampling time; and calculating the gas storage efficiency of the hydrate under the action of the promoter according to the trajectory data of the molecular dynamics simulation and a pre-constructed gas storage efficiency mathematical model. Based on the calculation result of the gas storage efficiency of the hydrate, the reliability of the promoter screening can be improved, and the screening process of the promoter can be accelerated.

The invention discloses a method for predicting the performance of a GO separation membrane based on molecular simulation and a preparation method of the GO separation membrane. Based on a molecular dynamics simulation technology, revealing a relationship among a GO interlayer water distribution state, an interlayer spacing and a chemical environment, and obtaining an interlayer water distribution state beneficial to ion transmission and corresponding geometric and chemical parameters of the membrane; the GO ion separation membrane is prepared under the guidance, a suspension of single-layer GO is prepared, a polyethersulfone membrane is adopted as a substrate, and thermal reduction treatment is performed after vacuum filtration and membrane formation. By reasonably regulating and controlling the interlayer spacing and the chemical environment of the GO membrane, fine regulation and control are carried out on the distribution state of water molecules containing interface bound water and non-interface free water in a confinement channel, and selective transmission of Li < + > relative to Na < + > and Mg < 2 + > by the GO membrane can be realized.