Design and screening method, device and storage medium of polymer gas separation membrane

Abstract

The present disclosure relates to the technical field of high-throughput molecular dynamics simulation, including a design and screening method, device, and storage medium for polymer gas separation membranes. The method involves obtaining monomer information of multiple polymer monomers; generating a membrane search space based on the monomer information; performing performance prediction on multiple candidate simulated polymer gas separation membranes in the membrane search space based on a pre-trained membrane performance prediction model; screening at least one target simulated polymer gas separation membrane that meets the desired performance from the membrane search space based on the performance prediction results; performing Monte Carlo simulation and molecular dynamics simulation on each target simulated polymer gas separation membrane using a molecular dynamics simulation tool to obtain a solubility coefficient and a diffusion coefficient; and determining the performance indicators of the target simulated polymer gas separation membrane based on the solubility coefficient and the diffusion coefficient. A large number of non-existing simulated membrane materials can be generated for analysis, thereby improving the efficiency of screening a large number of polymer materials.

Description

Technical Field

[0001] The present disclosure relates to the technical field of high-throughput molecular dynamics simulation, and in particular to a design and screening method, device, and storage medium for a polymer gas separation membrane. Background Art

[0002] Polymer gas separation membranes are a type of thin film material used for gas separation and purification. They are made of polymers and can selectively pass certain gas molecules based on their size, shape, and chemical properties. The gas separation principle of polymer gas separation membranes is generally based on a dissolution-diffusion mechanism, whereby gas molecules first dissolve on one side of the membrane, then diffuse within the membrane, and finally are released from the other side. Due to their low energy consumption, simple operation, and ease of large-scale production, polymer gas separation membranes are widely used in gas separation and purification applications such as natural gas processing, air separation, and pollution control.

[0003] Traditional polymer gas separation membranes have inherent physical and chemical properties that limit their separation efficiency and selectivity. For example, while improving permeability, these membranes often sacrifice selectivity. This is due to the material's microstructure and the diffusion mechanism of gas molecules. Therefore, to improve the separation performance of these membranes, it is necessary to optimize their structure and properties.

[0004] A typical design method for a polymer gas separation membrane includes: designing the polymer gas separation membrane based on the performance indicated by data in a polymer material database and literature.

[0005] However, most of the data in polymer material databases and literature are experimental values. These data are limited by the diversity of experimental conditions and methods, making it difficult to conduct large-scale material screening and optimization design. Summary of the Invention

[0006] In view of this, the present disclosure proposes a design and screening method, device and storage medium for polymer gas separation membranes, which can overcome the limitations of traditional molecular dynamics simulation in high-throughput processing, and at the same time combine machine learning technology to achieve rapid screening and accurate characterization of a large number of polymer materials, thereby achieving rapid, efficient and accurate screening and characterization of a large number of polymer materials.

[0007] According to one aspect of the present disclosure, a method for designing and screening a polymer gas separation membrane is provided, the method comprising:

[0008] Obtain monomer information of multiple polymer monomers;

[0009] generating a membrane search space based on the monomer information, wherein the membrane search space includes a plurality of candidate simulated polymer gas separation membranes;

[0010] Based on a pre-trained membrane performance prediction model, a plurality of candidate simulated polymer gas separation membranes in the membrane search space are predicted for performance, thereby obtaining a performance prediction result for each candidate simulated polymer gas separation membrane; the membrane performance prediction model is trained based on a machine learning algorithm;

[0011] screening at least one target simulated polymer gas separation membrane that meets desired performance from the membrane search space based on the performance prediction result;

[0012] performing a Monte Carlo simulation on each target simulated polymeric gas separation membrane based on a molecular dynamics simulation tool to determine the solubility coefficient of the target simulated polymeric gas separation membrane for different gases; and performing a molecular dynamics simulation on each target simulated polymeric gas separation membrane based on the molecular dynamics simulation tool to determine the diffusion coefficient of the target simulated polymeric gas separation membrane for different gases;

[0013] Based on the solubility coefficient and the diffusion coefficient, performance indicators of the target simulated polymer gas separation membrane are determined, and the performance indicators include gas permeability coefficient and / or membrane selectivity.

[0014] The performance prediction of multiple candidate simulated polymer gas separation membranes in the membrane search space is performed based on the pre-trained membrane performance prediction model to obtain a performance prediction result for each candidate simulated polymer gas separation membrane, including:

[0015] For each candidate simulated polymeric gas separation membrane, multi-scale information of the candidate simulated polymeric gas separation membrane is obtained; wherein the multi-scale information includes at least microscopic information and mesoscopic information; wherein the microscopic information is used to indicate the microscopic properties of the candidate simulated polymeric gas separation membrane, and the mesoscopic information is used to indicate the mesoscopic properties of the candidate simulated polymeric gas separation membrane;

[0016] The multi-scale information is input into the membrane performance prediction model to obtain the performance prediction result; wherein, the membrane performance prediction model is established based on a hierarchical graph convolutional neural network model.

[0017] In a possible implementation, the membrane performance prediction model includes, in order according to the information transmission direction:

[0018] An input layer, used for inputting the multi-scale information;

[0019] A convolutional layer, configured to extract features based on the multi-scale information;

[0020] An attention pooling layer, configured to determine an attention score of the feature data output by the convolutional layer; and perform weighted pooling on the feature data based on the attention score to obtain processed feature data;

[0021] The output layer is used to generate the performance prediction result based on the processed feature data.

[0022] In one possible implementation, the attention pooling layer is specifically used to determine the attention score of the feature data based on a multi-head attention mechanism guided by a mask matrix.

[0023] In a possible implementation, the microscopic information includes: a SMILES string of at least one polymer monomer forming the candidate simulated polymer gas separation membrane, and / or atom and bond information; wherein the atom and bond information is generated based on the SMILES string;

[0024] The mesoscopic information includes: the inter-monomer linking mode of the polymer monomers.

[0025] In a possible implementation, the multi-scale information further includes macroscopic information, where the macroscopic information is used to indicate macroscopic properties of the candidate simulated polymer gas separation membrane.

[0026] In one possible implementation, the macroscopic information includes: free volume fraction FFV and / or glass transition temperature Tg, where FFV refers to the proportion of space not occupied by molecules in the candidate simulated polymer gas separation membrane; the glass transition temperature Tg is the temperature at which the candidate simulated polymer gas separation membrane transitions from a glassy state to a rubbery state.

[0027] In a possible implementation, the method further includes: performing performance prediction on multiple candidate simulated polymer gas separation membranes in the membrane search space based on the pre-trained membrane performance prediction model, and obtaining the performance prediction result for each candidate simulated polymer gas separation membrane;

[0028] Generate samples to simulate polymer gas separation membranes;

[0029] Obtaining sample microscopic information and sample mesoscopic information of the sample simulated polymer gas separation membrane;

[0030] Simulating the molecular dynamics properties of each candidate simulated polymer gas separation membrane based on the molecular dynamics simulation tool to obtain simulated performance parameters of the sample simulated polymer gas separation membrane;

[0031] A pre-created hierarchical graph convolutional neural network model is trained based on the sample microscopic information, the sample mesoscopic information and the simulation performance parameters to obtain the membrane performance prediction model.

[0032] In a possible implementation, obtaining monomer information of a plurality of polymer monomers includes:

[0033] Receive the SMILES string of the polymer monomer;

[0034] and / or,

[0035] A polymer single chain model is received; and the polymer single chain model is parsed to obtain the monomer information.

[0036] According to another aspect of the present disclosure, a design and screening device for polymer gas separation membranes is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0037] According to another aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the above method when executed by a processor.

[0038] According to another aspect of the present disclosure, a computer program product is provided, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0039] The method obtains monomer information of multiple polymer monomers; generates a membrane search space based on the monomer information, wherein the membrane search space includes multiple candidate simulated polymer gas separation membranes; performs performance prediction on multiple candidate simulated polymer gas separation membranes in the membrane search space based on a pre-trained membrane performance prediction model, and obtains a performance prediction result of each candidate simulated polymer gas separation membrane; selects at least one target simulated polymer gas separation membrane that meets the expected performance from the membrane search space based on the performance prediction result; performs Monte Carlo simulation on each target simulated polymer gas separation membrane based on a molecular dynamics simulation tool to determine the performance of the target simulated polymer gas separation membrane for different gases. The solubility coefficient of the monomer is determined; molecular dynamics simulations are performed on each target simulated polymer gas separation membrane using a molecular dynamics simulation tool to determine the diffusion coefficient of the target simulated polymer gas separation membrane for different gases; based on the solubility coefficient and diffusion coefficient, the performance indicators of the target simulated polymer gas separation membrane are determined, including the gas permeability coefficient and / or membrane selectivity; a membrane search space can be generated based on monomer information, thereby forming a large number of non-existing simulated membrane materials for analysis, solving the problem of data being limited by the diversity of experimental conditions and methods, making large-scale material screening and optimization design difficult, and providing the possibility of designing new membrane materials. At the same time, the membrane performance prediction model is used to search for feasible target simulated polymer gas separation membranes in this membrane search space, eliminating the need to manually screen the performance of a large number of simulated membrane materials in the membrane search space, which can improve the efficiency of screening a large number of polymer materials.

[0040] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0042] Figure 1 A flow chart showing a method for designing and screening a polymer gas separation membrane according to an embodiment of the present disclosure;

[0043] Figure 2 A structural diagram showing a membrane performance prediction model according to an embodiment of the present disclosure;

[0044] Figure 3 A schematic diagram showing multi-scale information according to an embodiment of the present disclosure;

[0045] Figure 4 A schematic diagram showing microscopic information according to an embodiment of the present disclosure;

[0046] Figure 5A block diagram showing a design and screening device for a polymer gas separation membrane according to an embodiment of the present disclosure;

[0047] Figure 6 A block diagram illustrating a device for designing and screening a polymer gas separation membrane according to another embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0048] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0049] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0050] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0051] First, several terms involved in this application are introduced.

[0052] A polymer chain is a long chain of molecules made up of many repeating monomer units (i.e., polymer monomers) linked together by chemical bonds. These monomer units are linked together through polymerization to form a polymer with a specific molecular weight and structure. The length of polymer chains can vary from a few thousand atoms to millions of atoms, which gives polymers their unique physical and chemical properties.

[0053] A polymer monomer is the basic molecular unit that makes up a polymer chain. During polymerization, polymer monomers are linked together by covalent bonds to form long chains of polymer molecules (the polymer chain mentioned above). Each polymer monomer has one or more functional groups that can react with other monomers. For example, polyethylene is a polymer chain made from the polymerization of ethylene monomers. Each ethylene monomer has a double bond, which opens during polymerization and connects with the double bonds of other ethylene monomers to form long chains of polyethylene molecules.

[0054] Polymer single-chain model: A mathematical or computational model used to describe the physical and chemical properties of a single polymer chain. This model typically incorporates factors such as the polymer chain's conformation, flexibility, and interactions to predict and explain the macroscopic properties of polymer materials.

[0055] Hierarchical Graph Convolutional Networks (HGCN) are deep learning models that combine hierarchical structures with graph convolutional neural networks (GCNs). HGCNs are used to process graph data with hierarchical or layered structures, such as the multi-scale simulated polymer gas separation membrane structure data used in this application.

[0056] Figure 1 A flow chart of a method for designing and screening a polymer gas separation membrane according to an embodiment of the present disclosure is shown. In this embodiment, the method is described by taking an electronic device with computing capabilities as an example. The electronic device can be a user terminal or a server. The user terminal can be a computer, a tablet computer, etc. This embodiment does not limit the implementation of the electronic device. Figure 1 As shown, the method comprises at least the following steps:

[0057] Step 101: Acquire monomer information of a plurality of polymer monomers.

[0058] Monomer information is used to describe a polymer monomer. Illustratively, obtaining monomer information for multiple polymer monomers includes: receiving a Simplified Molecular Input Line Entry System (SMILES) string for the polymer monomer; and / or receiving a polymer single-chain model; and parsing the polymer single-chain model to obtain monomer information.

[0059] Optionally, the molecular dynamics simulation tool running in the electronic device provides a human-computer interaction interface to receive a SMILES string describing a polymer monomer or a polymer single chain model. In this embodiment, the molecular dynamics simulation tool can simulate various dynamic motions of the polymer gas separation membrane when separating gases. Among them, the molecular dynamics simulation tool includes but is not limited to: Large-scale Atomic / Molecular Massively Parallel Simulator (LAMMPS), Highly Optimized Object-oriented Many-particle Dynamics-blue (HOOMD-blue) and other open source molecular dynamics simulation software packages suitable for simulating dynamic systems composed of polymers and gases. This embodiment does not limit the implementation method of the molecular dynamics simulation tool.

[0060] Optionally, the polymer single chain model is parsed to obtain monomer information, including: identifying at least one repeating unit in the polymer single chain model to obtain at least one monomer; and generating a SMILES string describing each monomer to obtain monomer information.

[0061] Optionally, in other embodiments, the electronic device may also read monomer information of the polymer monomer from an existing material library.

[0062] Step 102 : generating a membrane search space based on monomer information, wherein the membrane search space includes a plurality of candidate simulated polymer gas separation membranes.

[0063] The membrane search space is used to provide a variety of possible simulated polymer gas separation membranes.

[0064] In one example, generating a membrane search space based on monomer information includes randomly selecting at least one monomer from a plurality of acquired monomer information, randomly arranging the monomers, and copolymerizing the monomers to form candidate simulated polymeric gas separation membranes. The monomer information constituting different candidate simulated polymeric gas separation membranes may differ, and / or the monomer information constituting different candidate simulated polymeric gas separation membranes may be arranged in different ways.

[0065] Schematically, the method of copolymerizing monomer information to generate candidate simulated polymer gas separation membranes includes: using RDKit and Open Babel toolkits to convert SMILES strings into simulated single-chain polymers to obtain candidate simulated polymer gas separation membranes.

[0066] In other embodiments, the method of generating the membrane search space based on monomer information may also be: generating candidate simulated polymer gas separation membranes based on monomer information and arrangement specified by the user. This embodiment does not limit the method of generating the membrane search space.

[0067] Optionally, after generating multiple candidate simulated polymeric gas separation membranes, the rationality of the candidate simulated polymeric gas separation membranes can be determined, and candidate simulated polymeric gas separation membranes whose rationality does not meet the desired target can be filtered to obtain a membrane search space. The rationality of the candidate simulated polymeric gas separation membranes can be represented by a value calculated using a Qe model or a Flory-Reaction Probability Factor model, and this embodiment does not limit the method for determining rationality.

[0068] Step 103 : Based on the pre-trained membrane performance prediction model, the performance of multiple candidate simulated polymer gas separation membranes in the membrane search space is predicted to obtain a performance prediction result for each candidate simulated polymer gas separation membrane.

[0069] The membrane performance prediction model is trained based on a machine learning algorithm, including but not limited to a graph neural network model and a hierarchical graph convolutional neural network model. This embodiment does not limit the implementation of the machine learning algorithm.

[0070] In one example, a membrane performance prediction model is established based on a hierarchical graph convolutional neural network model. Accordingly, the pre-trained membrane performance prediction model is used to predict the performance of multiple candidate simulated polymeric gas separation membranes in the membrane search space, obtaining performance prediction results for each candidate simulated polymeric gas separation membrane. This includes: obtaining multi-scale information about each candidate simulated polymeric gas separation membrane, including at least microscopic and mesoscopic information; and inputting the multi-scale information into the membrane performance prediction model to obtain performance prediction results.

[0071] The microscopic information is used to indicate the microscopic properties of the candidate simulated polymeric gas separation membrane. Illustratively, the microscopic information includes: a SMILES string of at least one polymer monomer forming the candidate simulated polymeric gas separation membrane, and / or atom and bond information. The atom and bond information is generated based on the SMILES string. Specifically, when the microscopic information includes atom and bond information, the electronic device obtains the SMILES string of at least one polymer monomer forming the candidate simulated polymeric gas separation membrane and generates the atom and bond information based on the SMILES string.

[0072] Illustratively, generating atom and bond information based on the SMILES string includes parsing the SMILES string to obtain atom information and bond information, wherein the atom information is used to describe the atom type, and the bond information is used to indicate the connection relationship between atoms. Optionally, the atom information includes, but is not limited to, the atomic symbol; and the bond information includes, but is not limited to, the bond type (e.g., single bond, double bond, triple bond, etc.).

[0073] Mesoscopic information is used to indicate the mesoscopic properties of the candidate simulated polymer gas separation membrane. Schematically, the mesoscopic information includes: the inter-monomer linkage mode of the polymer monomers. The inter-monomer linkage mode refers to: the specific way in which the polymer monomers are connected to each other during the polymerization process. The inter-monomer linkage mode describes which parts of the polymer monomers (such as functional groups) are involved in the connection and how they are arranged with each other. The inter-monomer linkage mode can affect the geometric shape, spatial arrangement and final physical properties of the polymer. For example: the inter-monomer linkage mode includes but is not limited to: head-to-tail linkage, head-to-head linkage, tail-to-tail linkage, alternate linkage, branching linkage, cross-linking, cyclic linkage, etc. This embodiment does not list the inter-monomer linkage modes one by one. Optionally, the inter-monomer linkage mode can be randomly generated, or it can be received based on the human-computer interaction interface. This embodiment does not limit the method for obtaining the inter-monomer linkage mode.

[0074] Optionally, the multi-scale information also includes macroscopic information, which indicates the macroscopic properties of the candidate simulated polymeric gas separation membrane. Illustratively, the macroscopic information includes the free volume fraction (FFV) and / or the glass transition temperature (Tg). FFV refers to the fraction of space in the candidate simulated polymeric gas separation membrane that is not occupied by molecules, and the glass transition temperature (Tg) is the temperature at which the candidate simulated polymeric gas separation membrane transitions from a glassy state to a rubbery state.

[0075] In one example, FFV and Tg are obtained by performing molecular dynamics simulation on a candidate simulated polymer gas separation membrane using a molecular dynamics simulation tool.

[0076] The candidate simulated polymer gas separation membrane is subjected to molecular dynamics simulation using a molecular dynamics simulation tool, including: molecular dynamics simulation of the candidate simulated polymer gas separation membrane using an optimized liquid simulation all-atom (OPLS-AA) force field for all-atom molecular dynamics (MD) simulation.

[0077] Since OPLS-AA is more effective and robust in simulating various molecular systems, its application in the polymer-gas simulation system of this embodiment can improve simulation accuracy.

[0078] Specifically, taking the molecular dynamics simulation tool as the LAMMPS software package as an example, the simulation process includes the following steps 31 and 32:

[0079] Step 31, generating a periodic molecular box suitable for LAMMPS simulation;

[0080] Before starting a simulation using LAMMPS, define a three-dimensional spatial region, which is the simulation box (or simulation unit). Place the simulated molecules corresponding to the candidate simulated polymer gas separation membrane currently being simulated in this region for subsequent simulation calculations to obtain a molecular box.

[0081] When creating a simulation box, it is necessary to specify the boundary conditions of the simulation unit. Common boundary conditions include periodic boundary conditions and non-periodic boundary conditions. Periodic boundary conditions indicate that when a particle leaves one boundary of the simulation box, it will re-enter the simulation unit from the opposite boundary, so that an infinite system can be simulated. Non-periodic boundary conditions do not have such restrictions. Non-periodic boundary conditions indicate that the boundary of the simulation box will not be regarded as part of the continuous space, that is, the particles in the simulation box will not jump to the other side of the box when they reach the boundary, but will be affected by the boundary, such as being reflected or absorbed. Schematically, in this embodiment, the boundary condition of the periodic molecular box is an infinite periodic molecular box, that is, the boundary of the simulation box is regarded as extending infinitely, and the particles will immediately appear at the opposite boundary when they reach the boundary on one side, thereby simulating an infinite system.

[0082] Optionally, before step 31, the simulation system may be subjected to energy minimization and annealing processes to eliminate any unnatural atomic overlaps or high energy states through the energy minimization process, and the system temperature may be slowly lowered through the annealing process to ensure that the simulation system reaches a thermodynamic equilibrium state.

[0083] Step 32, execute the run file for molecular dynamics simulation, simulate the temperature change process through the heating sequence and cooling sequence in the run file; process the density of the candidate simulated polymer gas separation membrane recorded during the temperature change process through a customized analysis script, and determine the glass transition temperature Tg of the candidate simulated polymer gas separation membrane based on the density; and / or, process the ratio of the volume occupied by atoms and the volume of the pores in the candidate simulated polymer gas separation membrane during the temperature change process through a customized analysis script to obtain FFV.

[0084] In this embodiment, the operation file includes a heating sequence and a cooling sequence for simulating a temperature change process during the molecular dynamics simulation.

[0085] Illustratively, the temperature change process simulated by the heating and cooling sequences includes: starting from a preset initial temperature and gradually increasing to a preset maximum temperature at preset temperature intervals. The maximum temperature is greater than the initial temperature; the initial temperature may be 200 Kelvin (K) and the maximum temperature may be 1000 K. In other embodiments, the initial and maximum temperatures may also be other values, and this embodiment does not limit the values ​​of the initial and maximum temperatures.

[0086] During the glass transition, the volume of the candidate simulated polymer gas separation membrane changes significantly, and consequently, its density changes significantly. Based on this, after the temperature ramp is complete, the recorded density is processed by an analysis script. The script calculates the discrete derivative (or slope) of density as a function of temperature and determines the temperature at which the slope is maximum (or the rate of change in density is maximum) as the glass transition temperature (Tg). Furthermore, the analysis script processes the recorded atomically occupied volume and pore volume, and then calculates the ratio of the atomically occupied volume to the pore volume to obtain the FFV.

[0087] Optionally, the run file also includes other files required for the molecular dynamics simulation process, such as: topology files, coordinate files and parameter files for each polymer, etc., to indicate how to construct the force field model of the molecule, determine the position and direction of the molecule, define the topological information and parameters of a single molecule type, etc. This embodiment does not limit the content included in the run file.

[0088] In this embodiment, reference Figure 2 ,The pre-trained membrane performance prediction model includes the following order according to the information transmission direction: an input layer 210, a convolutional layer 220, an attention pooling layer 230, and an output layer 240.

[0089] The input layer 210 is used to input multi-scale information. In this embodiment, the electronic device labels the attributes of the different scale information in the multi-scale information to record the source and level of each information. The attributes of the different scale information remain unchanged during the prediction process.

[0090] Taking multi-scale information including microscopic information, mesoscopic information and macroscopic information as an example, refer to Figure 3 The schematic diagram of the 3-layer input configuration of the input layer 210 shown in FIG. Figure 3 It can be seen that the information input by the input layer 210 includes three levels. The scale information of the third level is microscopic information. Accordingly, the attributes of the third level indicate that the microscopic information belongs to the third level. The microscopic information includes a SMILES string of at least one polymer monomer that forms a candidate simulated polymer gas separation membrane. Then, the input layer 210 generates the atomic and bond information of each polymer monomer based on the SMILES string. Figure 4This atom and bond information includes node features, node labels, edge features, and edge weights. Node features include atomic charge and atom type (e.g., hydrogen mixture, aromaticity), while node labels include atomic number. Edge features include bond type and conjugation form, while edge weights include steric effects.

[0091] When constructing atom and bond information, if the candidate simulated polymer gas separation membrane is generated from at least two different polymer monomer units, an additional edge can be added to indicate the connection method of the different types of polymer monomer units, indicating that each polymer monomer unit may also be connected to other monomers. In this way, when the membrane performance prediction model analyzes one polymer monomer unit, it can take into account the existence of other monomers based on this additional edge, allowing the end atoms to understand the local chemical environment. Among them, the polymer monomer unit refers to a unit composed of multiple repeated polymer monomers. This representation method performs better than using virtual atoms to represent the monomer ends. For example: the polymer chain corresponding to the candidate simulated polymer gas separation membrane is: AAABBABCCCBBAAA, where A, B and C represent different polymer monomer units. At this time, an edge can be connected between each of the polymer monomer units A, B and C to define the corresponding connection method, for example: an edge is connected between the polymer monomer units A and B to define that the polymer monomer units A and B are connected as a CC bond; an edge is connected between the polymer monomer units A and B to define that the polymer monomer units A and B are connected as a CN bond; an edge is connected between the polymer monomer units B and C to define that the polymer monomer units B and C are connected as a CN bond.

[0092] The input layer 210 of this embodiment supports the input of multiple SMILES strings, such as: "monomer SMILES1.monomer SMILES2.monomer SMILES3.monomer SMILES4", while the traditional neural network model only allows the input of a single SMILES string. At this time, it can only predict the performance of a candidate simulated polymer gas separation membrane formed by the polymerization of a single polymer monomer. This embodiment supports the prediction of the performance of a candidate simulated polymer gas separation membrane formed by the polymerization of multiple polymer monomers, expanding the application scenarios of the model.

[0093] The second-level attribute indicates that mesoscopic information belongs to the second level, and the mesoscopic information includes the inter-monomer linking mode of the polymer monomers corresponding to the atoms and bonds indicated by the microscopic information.

[0094] The first-level attribute indicates that macroscopic information belongs to the first level, and the macroscopic information includes Tg and FFV of the candidate simulated polymer gas separation membrane composed of the polymer monomer corresponding to the atoms and bonds indicated by the microscopic information.

[0095] Based on this, the input layer 210 receives input information and associates multi-scale information for performance prediction by the hierarchical graph convolutional neural network. The performance of the membrane material can be predicted by combining the different scale characteristics of the candidate simulated polymer gas separation membrane to improve the prediction accuracy.

[0096] Optionally, in order to process attributes of different scales, the input layer 210 includes a multilayer perceptron (MLP) to preprocess multi-scale information so that each attribute undergoes a nonlinear transformation before being input into the convolutional layer to obtain a processed feature representation, which is uniformly input into the same convolutional layer to ensure the integrity and coherence of the information.

[0097] Optionally, the input layer 210 is further connected to an embedding layer to map the input information received by the input layer 210 into a continuous vector space (or embedding space) to improve the model's ability to understand the input information, thereby improving model performance.

[0098] Convolutional layer 220 is used to extract features based on multi-scale information. Figure 3 In this embodiment, the convolutional layer is a Graph Convolutional Network (GCN) layer. The GCN layer updates the feature representation of the node by performing specific operations on the node features and the adjacency matrix.

[0099] The attention pooling layer 230 is used to determine the attention score of the feature data output by the convolution layer; and perform weighted pooling on the feature data based on the attention score to obtain processed feature data.

[0100] Optionally, the attention pooling layer is specifically configured to determine an attention score for the feature data based on a multi-head attention mechanism guided by a mask matrix. That is, for each head in the multi-head attention mechanism, after determining the attention score corresponding to the head, the mask matrix is ​​used to set the attention score between a preset feature position and other feature positions to a preset smaller value (e.g., negative infinity) to obtain an updated attention score; and the updated attention score is used to perform weighted pooling on the feature data to obtain processed feature data.

[0101] The output layer 240 is used to generate performance prediction results based on the processed feature data.

[0102] Optionally, the performance prediction result is determined based on the performance prediction requirement. For example, if the performance prediction requirement is to predict the Tg of a candidate simulated polymer gas separation membrane, the membrane performance prediction model is trained using at least Tg as a label, and the performance prediction result includes parameters indicating Tg. For another example, if the performance prediction requirement is to predict the solubility of a candidate simulated polymer gas separation membrane, the membrane performance prediction model is trained using at least solubility as a label, and the performance prediction result includes parameters indicating solubility. Furthermore, there can be one or more performance prediction results. In this case, the membrane performance prediction model is a multi-task learning model.

[0103] The training process of the membrane performance prediction model is detailed in the following embodiment, which will not be repeated here.

[0104] Step 104 : Screen out at least one target simulated polymer gas separation membrane that meets the desired performance from the membrane search space based on the performance prediction result.

[0105] The expected performance is preset in the electronic device, and the electronic device compares the performance prediction result with the expected performance. If the performance prediction result matches the expected performance, the candidate simulated polymer gas separation membrane corresponding to the performance prediction result is determined as the target simulated polymer gas separation membrane.

[0106] In this embodiment, a large number of candidate simulated polymer gas separation membranes in the membrane search space are screened through the membrane performance prediction model, and the screened target simulated polymer gas separation membranes are designed. This can filter out some candidate simulated polymer gas separation membranes that cannot be designed, thereby improving the design efficiency of polymer gas separation membranes.

[0107] Step 105: Perform Monte Carlo simulation on each target simulated polymer gas separation membrane based on the molecular dynamics simulation tool to determine the solubility coefficient of the target simulated polymer gas separation membrane for different gases; and perform molecular dynamics simulation on each target simulated polymer gas separation membrane based on the molecular dynamics simulation tool to determine the diffusion coefficient of the target simulated polymer gas separation membrane for different gases.

[0108] The molecular dynamics simulation tool is described in detail in the above embodiment, and will not be described in detail in this embodiment.

[0109] In one example, the solubility coefficient of the target simulated polymer gas separation membrane is first determined, and then the diffusion coefficient of the target simulated polymer gas separation membrane is determined.

[0110] Performing a Monte Carlo simulation on each target simulated polymer gas separation membrane to obtain the solubility coefficient of the target simulated polymer gas separation membrane for different gases includes the following steps 51-58:

[0111] Step 51, performing energy minimization and annealing processes on the simulation system to obtain a stabilized system;

[0112] The relevant descriptions of the energy minimization and annealing process are detailed in the above embodiment, and will not be repeated here in this embodiment.

[0113] Step 52, performing a constant number, volume and temperature (NVT) simulation on the stabilized system;

[0114] Among them, NVT simulation refers to molecular dynamics simulation performed under constant conditions of atomic number (N), volume (V) and temperature (T).

[0115] For example, the electronic device performs a 0.5 nanosecond NVT simulation at 500 K on a stabilized system. In other embodiments, the simulation duration and temperature may also be other values, and this embodiment does not limit the values ​​of the simulation duration and temperature.

[0116] Step 53, performing a constant number of atoms, pressure and temperature (NPT) simulation on the system after NVT simulation;

[0117] Among them, NPT simulation refers to molecular dynamics simulation performed under constant conditions of atomic number (N), pressure (P) and temperature (T).

[0118] For example, the electronic device performs an NPT simulation on the system after NVT simulation at 0.5 nanoseconds, 500K, and 1 bar. In other embodiments, the simulation duration and temperature may also be other values, and this embodiment does not limit the values ​​of the simulation duration, temperature, and pressure.

[0119] Step 54, performing x thermal annealing cycles on the system after NPT simulation to bring the temperature of the simulation system to a first preset temperature, maintaining the pressure at a preset pressure value during the annealing process, and performing NVT simulation and NPT simulation on the annealed simulation system again;

[0120] The first preset temperature may be 250K. In other embodiments, the first preset temperature may also be other values. This embodiment does not limit the value of the first preset temperature. x is the number of cycles required to reduce the temperature of the simulation system to 250K. For example, if the initial temperature of the simulation system is 500K and each thermal annealing cycle reduces the temperature by 50K, then the value of x is 5. The preset pressure may be 1 bar.

[0121] The duration of NVT simulation and NPT simulation can be the same as the above steps, for example: 0.5 nanoseconds. In other embodiments, the duration can also be other values. This embodiment does not limit the duration of NVT simulation and NPT simulation.

[0122] Step 55 : determining the solubility coefficients of the target simulated polymer gas separation membrane for different gases.

[0123] At infinite dilution, the solubility coefficient can be expressed by the Henry's constant of the target simulated polymeric gas separation membrane. The Henry's constant for a polymeric gas separation membrane is the proportional constant between the solubility of a gas in the polymer membrane material and its partial pressure at the membrane surface under specific temperature and pressure conditions. The solubility coefficient is the limiting value of the ratio of the gas concentration in the target simulated polymeric gas separation membrane to its partial pressure at the membrane surface. The solubility coefficient and Henry's constant are numerically inversely proportional: a larger Henry's constant indicates a smaller solubility coefficient, indicating a lower solubility of the gas in the target simulated polymeric gas separation membrane.

[0124] Schematically, the solubility coefficient S corresponding to the i-th gas i It is expressed by the following formula:

[0125]

[0126] Among them, c i represents the concentration of the i-th gas in the target simulated polymer gas separation membrane, f i represents the partial pressure of the i-th gas in the membrane surface, the limit c i →0 represents the situation when the gas concentration approaches 0, that is, under the condition of infinite dilution. i is a positive integer.

[0127] Step 56, after completing the solubility calculation, introduce gas molecules into the molecular box containing the target simulated polymer gas separation membrane; and perform energy minimization and annealing processes on the simulated system filled with gas to enable the molecules in the simulated system to reach a stable or equilibrium state;

[0128] The gas molecules may be CO2 and / or N2, etc., or may be gases in the actual working environment of the target simulated polymer gas separation membrane. This embodiment does not limit the type of gas molecules.

[0129] Step 57 : performing NVT simulation and NPT simulation at the second preset temperature.

[0130] The second preset temperature may be 300K. In other embodiments, the second preset temperature may also be other values. This embodiment does not limit the value of the second preset temperature.

[0131] For example, the electronic device first performs an NVT simulation for 1 nanosecond; then performs an NPT simulation at 300K and 1 atmosphere for 2 nanoseconds. In other embodiments, the duration of the NVT and NPT simulations in this step can also be other durations, as long as the duration allows the system to reach stability.

[0132] Step 58 , running the simulation system for a preset maintenance time, and collecting and analyzing data during the preset maintenance time to obtain a mean squared displacement (MSD), and determining the diffusion coefficient of the target simulated polymer gas separation membrane for different gases based on the mean squared displacement.

[0133] The preset hold time is used to allow the simulation system to reach equilibrium and complete data collection and analysis. For example, if the preset hold time is 50 nanoseconds, the first 2 nanoseconds are used for equilibrium, and the remaining 48 nanoseconds are used for data collection and analysis. In actual implementation, the preset hold time may also be other values, and this embodiment does not impose any restrictions on the value of the preset hold time.

[0134] The diffusion coefficient is used to describe the rate of random movement of gas in the target simulated polymer gas separation membrane.

[0135] The diffusion coefficient D corresponding to the i-th gas i It can be expressed by the following formula:

[0136]

[0137] Where t represents time, r(t) represents the position vector of the particle of the i-th gas at time t; r(0) represents the position vector of the particle of the i-th gas at the initial time (t=0); |r(t)-r(0)| 2 represents the square of the distance between the particle's position at time t and the initial moment, and <> represents the statistical average, that is, the average of the results of all particles or multiple experiments.

[0138] According to the above formula, the diffusion coefficient is one-sixth of the mean square displacement of the particle over an infinite time period. In practical applications, since it is impossible to measure an infinite time period, the MSD is measured over a sufficiently long time scale and the diffusion coefficient is estimated through linear fitting. Ideally, the MSD has a linear relationship with time, that is, <|r(t)-r(0)| 2 >=6D i t, that is, in three-dimensional space, the MSD of the particle increases linearly with time, and the slope of MSD is the diffusion coefficient D i .

[0139] Step 106 : Determine the performance index of the target simulated polymer gas separation membrane based on the solubility coefficient and the diffusion coefficient. The performance index includes the gas permeability coefficient and / or the membrane selectivity.

[0140] The permeability coefficient is used to describe the rate at which gas passes through the target simulated polymer gas separation membrane. The permeability coefficient combines the solubility and diffusion capacity of the gas in the target simulated polymer gas separation membrane. For the i-th gas, the permeability coefficient Pi It can be expressed by the following formula:

[0141] P i =S i ·D i ;

[0142] Among them, S i represents the solubility coefficient corresponding to the i-th gas; D i represents the diffusion coefficient corresponding to the i-th gas.

[0143] Selectivity is used to describe the separation ability of a target simulated polymer gas separation membrane for different gases. Generally, selectivity is expressed as the ratio of the permeability coefficients of two gases.

[0144] Schematically, the selectivity α for gas i relative to gas j is i / j , the selectivity can be expressed by the following formula:

[0145] α i / j =P i / P j ;

[0146] Among them, P i represents the permeability coefficient corresponding to the i-th gas, P j represents the permeability coefficient corresponding to the j-th gas, where j is a positive integer different from the value of i.

[0147] In summary, the design and screening method of the polymer gas separation membrane provided in this embodiment obtains monomer information of multiple polymer monomers; generates a membrane search space based on the monomer information, and the membrane search space includes multiple candidate simulated polymer gas separation membranes; performs performance prediction on multiple candidate simulated polymer gas separation membranes in the membrane search space based on a pre-trained membrane performance prediction model to obtain a performance prediction result for each candidate simulated polymer gas separation membrane; screens out at least one target simulated polymer gas separation membrane that meets the expected performance from the membrane search space based on the performance prediction result; performs Monte Carlo simulation on each target simulated polymer gas separation membrane based on a molecular dynamics simulation tool to determine the target The solubility coefficients of polymer gas separation membranes for different gases are simulated. Molecular dynamics simulations are then performed on each target simulated polymer gas separation membrane using molecular dynamics simulation tools to determine the diffusion coefficients of the target simulated polymer gas separation membrane for different gases. Based on the solubility coefficient and diffusion coefficient, the performance indicators of the target simulated polymer gas separation membrane are determined, including gas permeability coefficients and / or membrane selectivity. A membrane search space can be generated based on monomer information, thereby forming a large number of non-existing simulated membrane materials for analysis. This solves the problem of data being limited by the diversity of experimental conditions and methods, making large-scale material screening and optimization design difficult, and provides the possibility for designing new membrane materials. At the same time, a membrane performance prediction model is used to search for feasible target simulated polymer gas separation membranes in this membrane search space, eliminating the need to manually screen the performance of a large number of simulated membrane materials in the membrane search space, which can improve the efficiency of screening a large number of polymer materials.

[0148] In addition, a membrane performance prediction model is established based on a hierarchical graph convolutional neural network model to input the multi-scale information of the simulated membrane material into the membrane performance prediction model for performance prediction, so that the membrane performance prediction model can perform performance prediction based on the comprehensive characteristics of the simulated membrane material, thereby improving the accuracy of performance prediction.

[0149] In addition, by setting an attention pooling layer in the membrane performance prediction model, the membrane performance prediction model can be dynamically weighted according to the importance of the features, effectively retaining more key information and filtering out redundant information, thereby improving the accuracy of performance prediction and improving computational efficiency.

[0150] In addition, by setting the attention pooling layer based on a multi-head attention mechanism guided by a mask matrix to determine the attention score of the feature data, the impact and importance of attribute changes at different levels on performance prediction can be analyzed, allowing the model to dynamically adjust the importance of features, thereby more accurately capturing key features and improving model accuracy.

[0151] In addition, by manually constructing a polymer single chain model or a polymer monomer described by a SMILES string, non-professionals can also use the method provided in this embodiment to input monomer information, thereby improving the applicability of the method.

[0152] Based on the above embodiment, before step 103, that is, based on the pre-trained membrane performance prediction model, the performance of multiple candidate simulated polymer gas separation membranes in the membrane search space is predicted, and before the performance prediction result of each candidate simulated polymer gas separation membrane is obtained, it also includes training to obtain the membrane performance prediction model.

[0153] The training process of the membrane performance prediction model includes the following steps:

[0154] Step 1: Generate a sample to simulate a polymer gas separation membrane.

[0155] Among them, the method of generating the sample simulated polymer gas separation membrane is the same as the method of generating the candidate simulated polymer gas separation membrane, that is, it is generated based on the acquired monomer information; or, the sample simulated polymer gas separation membrane is a simulation model corresponding to the existing polymer gas separation membrane. This embodiment does not limit the method of obtaining the sample simulated polymer gas separation membrane.

[0156] Step 2: Obtain sample microscopic information and sample mesoscopic information of the sample simulating the polymer gas separation membrane.

[0157] The method for acquiring the sample microscopic information and the sample mesoscopic information is the same as the method for acquiring the microscopic information and the mesoscopic information of the candidate simulated polymer gas separation membrane. The information types are consistent and will not be described in detail in this embodiment.

[0158] Step 3: Using a molecular dynamics simulation tool, simulate the molecular dynamics properties of each sample simulated polymer gas separation membrane to obtain simulated performance parameters of the candidate simulated polymer gas separation membrane.

[0159] In this embodiment, simulated performance parameters can be obtained by simulating the molecular dynamics properties of a sample simulated polymer gas separation membrane using a molecular dynamics simulation tool. Data collected and analyzed during the simulation process yield simulated performance parameters. The simulated performance parameters share the same prediction objectives as the membrane performance prediction model. For example, if the membrane performance prediction model is used to predict the Tg, solubility coefficient, and diffusion coefficient of a candidate simulated polymer gas separation membrane, the simulated performance parameters would include the Tg, solubility coefficient, and diffusion coefficient of the sample simulated polymer gas separation membrane.

[0160] Step 4: Train a pre-created hierarchical graph convolutional neural network model based on the sample microscopic information, sample mesoscopic information and simulation performance parameters to obtain a membrane performance prediction model.

[0161] Specifically, after inputting the sample microscopic information and sample mesoscopic information into a pre-created hierarchical graph convolutional neural network model, the model's prediction results are obtained, and the prediction results are compared with the simulation performance parameters; based on the comparison results, the model parameters of the hierarchical graph convolutional neural network model are iteratively optimized to obtain a membrane performance prediction model.

[0162] Figure 5 A block diagram of a design and screening device for a polymer gas separation membrane according to an embodiment of the present disclosure is shown. Figure 5 As shown, the device includes the following modules: an information acquisition module 510 , a space generation module 520 , a performance prediction module 530 , a membrane screening module 540 , a motion simulation module 550 and a performance determination module 560 .

[0163] An information acquisition module 510 is used to acquire monomer information of a plurality of polymer monomers;

[0164] A space generation module 520 is configured to generate a membrane search space based on the monomer information, wherein the membrane search space includes a plurality of candidate simulated polymer gas separation membranes;

[0165] a performance prediction module 530 for performing performance prediction on a plurality of candidate simulated polymer gas separation membranes in the membrane search space based on a pre-trained membrane performance prediction model, and obtaining a performance prediction result for each candidate simulated polymer gas separation membrane; the membrane performance prediction model is trained based on a machine learning algorithm;

[0166] a membrane screening module 540 for screening at least one target simulated polymer gas separation membrane that meets the desired performance from the membrane search space based on the performance prediction result;

[0167] a motion simulation module 550 for performing a Monte Carlo simulation on each target simulated polymeric gas separation membrane based on a molecular dynamics simulation tool to determine the solubility coefficient of the target simulated polymeric gas separation membrane for different gases; and performing a molecular dynamics simulation on each target simulated polymeric gas separation membrane based on the molecular dynamics simulation tool to determine the diffusion coefficient of the target simulated polymeric gas separation membrane for different gases;

[0168] The performance determination module 560 is configured to determine performance indicators of the target simulated polymer gas separation membrane based on the solubility coefficient and the diffusion coefficient, wherein the performance indicators include gas permeability coefficient and / or membrane selectivity.

[0169] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0170] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium.

[0171] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0172] An embodiment of the present disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0173] Figure 6 1 is a block diagram of an apparatus 1900 for designing and screening a polymer gas separation membrane according to an exemplary embodiment. For example, the apparatus 1900 can be provided as a server or a terminal device. Figure 6 The apparatus 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions, such as an application, that can be executed by the processing component 1922. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described method.

[0174] The device 1900 may also include a power supply component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input / output interface 1958 (I / O interface). The device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server 2003. TM , MacOS X TM , Unix TM ,Linux TM , FreeBSD TM or similar.

[0175] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by the processing component 1922 of the apparatus 1900 to perform the above-described method.

[0176] While various embodiments of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for designing and screening a polymer gas separation membrane, characterized in that: The method comprises: Obtain monomer information of multiple polymer monomers; generating a membrane search space based on the monomer information, wherein the membrane search space includes a plurality of candidate simulated polymer gas separation membranes; Based on a pre-trained membrane performance prediction model, a plurality of candidate simulated polymer gas separation membranes in the membrane search space are predicted for performance, thereby obtaining a performance prediction result for each candidate simulated polymer gas separation membrane; the membrane performance prediction model is trained based on a machine learning algorithm; screening at least one target simulated polymer gas separation membrane that meets desired performance from the membrane search space based on the performance prediction result; performing a Monte Carlo simulation on each target simulated polymeric gas separation membrane based on a molecular dynamics simulation tool to determine the solubility coefficient of the target simulated polymeric gas separation membrane for different gases; and performing a molecular dynamics simulation on each target simulated polymeric gas separation membrane based on the molecular dynamics simulation tool to determine the diffusion coefficient of the target simulated polymeric gas separation membrane for different gases; Based on the solubility coefficient and the diffusion coefficient, performance indicators of the target simulated polymer gas separation membrane are determined, and the performance indicators include gas permeability coefficient and / or membrane selectivity.

2. The method according to claim 1, characterized in that The performance prediction of multiple candidate simulated polymer gas separation membranes in the membrane search space is performed based on the pre-trained membrane performance prediction model to obtain a performance prediction result for each candidate simulated polymer gas separation membrane, including: For each candidate simulated polymeric gas separation membrane, multi-scale information of the candidate simulated polymeric gas separation membrane is obtained; wherein the multi-scale information includes at least microscopic information and mesoscopic information; wherein the microscopic information is used to indicate the microscopic properties of the candidate simulated polymeric gas separation membrane, and the mesoscopic information is used to indicate the mesoscopic properties of the candidate simulated polymeric gas separation membrane; The multi-scale information is input into the membrane performance prediction model to obtain the performance prediction result; wherein, the membrane performance prediction model is established based on a hierarchical graph convolutional neural network model.

3. The method according to claim 2, characterized in that The membrane performance prediction model includes the following in order according to the information transmission direction: An input layer, used for inputting the multi-scale information; A convolutional layer, configured to extract features based on the multi-scale information; An attention pooling layer, configured to determine an attention score of the feature data output by the convolutional layer; and perform weighted pooling on the feature data based on the attention score to obtain processed feature data; The output layer is used to generate the performance prediction result based on the processed feature data.

4. The method according to claim 3, characterized in that The attention pooling layer is specifically used to determine the attention score of the feature data based on a multi-head attention mechanism guided by a mask matrix.

5. The method according to claim 2, characterized in that The microscopic information includes: a SMILES string of at least one polymer monomer forming the candidate simulated polymer gas separation membrane, and / or atom and bond information; wherein the atom and bond information is generated based on the SMILES string; The mesoscopic information includes: the inter-monomer linking mode of the polymer monomers.

6. The method according to claim 2, characterized in that The multi-scale information further includes macroscopic information, where the macroscopic information is used to indicate macroscopic properties of the candidate simulated polymer gas separation membrane.

7. The method according to claim 6, characterized in that The macroscopic information includes: free volume fraction FFV and / or glass transition temperature Tg, wherein FFV refers to the proportion of space not occupied by molecules in the candidate simulated polymer gas separation membrane; the glass transition temperature Tg is the temperature at which the candidate simulated polymer gas separation membrane transforms from a glassy state to a rubbery state.

8. The method according to claim 2, characterized in that Before performing performance prediction on a plurality of candidate simulated polymer gas separation membranes in the membrane search space based on the pre-trained membrane performance prediction model and obtaining a performance prediction result for each candidate simulated polymer gas separation membrane, the method further includes: Generate samples to simulate polymer gas separation membranes; Obtaining sample microscopic information and sample mesoscopic information of the sample simulated polymer gas separation membrane; Simulating the molecular dynamics properties of each candidate simulated polymer gas separation membrane based on the molecular dynamics simulation tool to obtain simulated performance parameters of the sample simulated polymer gas separation membrane; A pre-created hierarchical graph convolutional neural network model is trained based on the sample microscopic information, the sample mesoscopic information and the simulation performance parameters to obtain the membrane performance prediction model.

9. The method according to any one of claims 1 to 8, characterized in that: The obtaining of monomer information of a plurality of polymer monomers includes: Receive the SMILES string of the polymer monomer; and / or, A polymer single chain model is received; and the polymer single chain model is parsed to obtain the monomer information.

10. A design and screening device for polymer gas separation membranes, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method according to any one of claims 1 to 9 when executing the instructions stored in the memory.

11. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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