Method for simulating sodium ion battery to form solid electrolyte interface, medium and program product

Abstract

The invention relates to a method for simulating a sodium ion battery to form a solid electrolyte interface, a medium and a program product, and the method comprises the following steps: generating an initial model: generating an initial sodium ion battery model by using Packmol software, defining the size and boundary conditions of a simulation system, and setting the number and distribution of molecules; setting force field parameters; performing simulation operation, namely operating the adjusted initial sodium ion battery model by using an LAMMPS command, and recording the locus and energy change of atoms in the simulation process; and data analysis: performing post-processing analysis on the recorded track and energy change data by using ReacNetGenerator software, and analyzing a chemical reaction path, energy change and product distribution in the SEI forming process. Compared with the prior art, the method can be used for more comprehensively understanding and predicting products and reactions in the sodium ion battery in the SEI forming process, so that theoretical support is provided for design and optimization of the sodium ion battery.

Description

Technical Field

[0001] The present invention relates to the technical field of sodium ion batteries, and in particular to a method, a medium and a program product for simulating a sodium ion battery to form a solid electrolyte interface. Background Art

[0002] With the growing demand for energy storage, sodium-ion batteries (SIBs) have attracted widespread attention as a promising energy storage technology due to their low cost and abundant sodium resources. In the practical application of sodium-ion batteries, the formation of the solid electrolyte interface (SEI) has a crucial impact on the battery performance. The SEI layer can stabilize the battery's charge and discharge process and prevent the continuous decomposition of the electrolyte, thereby extending the battery's cycle life. However, the formation process of the SEI layer is complex and involves multiple chemical reactions and the transport of substances, which makes it very difficult to understand and control its formation mechanism.

[0003] At present, the understanding of the SEI formation process mainly relies on experimental testing and characterization methods, such as X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM) and cryo-electron microscopy. Although these methods can provide composition and structural information of the SEI layer, they are often unable to monitor the dynamic changes during the SEI formation process in real time, nor can they reveal the detailed chemical mechanism of SEI formation from a microscopic level. In addition, theoretical calculation methods, such as first principles (DFT), ab initio molecular dynamics (AIMD) and classical molecular dynamics (MD), can provide detailed information at the atomic level, but they face the problems of high computational cost and low computational efficiency when dealing with large-scale systems and long time scale simulations. Especially for processes such as SEI formation involving a large number of atoms and complex chemical reactions, traditional calculation methods are often unable to cope with it.

[0004] In addition, existing simulation methods often use a single simulation method, such as MD alone or ReaxFF-MD, which has limitations in comprehensively predicting and analyzing the various products during SEI formation. For example, although ReaxFF-MD can handle larger systems and longer time scales, it is not accurate enough in describing the electrochemical kinetics and charge transfer during SEI formation.

[0005] Therefore, there is an urgent need to develop a new simulation method that can comprehensively consider the various physical and chemical factors in the SEI formation process and provide more comprehensive and accurate simulation results, which is of great significance for understanding and optimizing the performance of sodium-ion batteries. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method, medium, and program product for simulating the formation of a solid electrolyte interface in a sodium ion battery, which can more comprehensively understand and predict the products and reactions in the SEI formation process in a sodium ion battery, thereby providing theoretical support for the design and optimization of sodium ion batteries.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A first aspect of the present invention provides a method for simulating a sodium ion battery to form a solid electrolyte interface, the method comprising the following steps:

[0009] S1. Initial model generation, in which Packmol software is used to generate the initial sodium-ion battery model, define the size and boundary conditions of the simulation system, set the number and distribution of molecules, and ensure that there is no unreasonable contact;

[0010] S2, force field parameter setting, wherein an appropriate ReaxFF force field file is selected and force field parameters are adjusted according to simulation requirements to adjust the initial sodium ion battery model generated in S1;

[0011] S3, simulation run, in which the initial sodium ion battery model adjusted in S2 is run using the LAMMPS command, and the trajectory and energy changes of atoms during the simulation are recorded;

[0012] S4. Data analysis, in which ReacNetGenerator software is used to post-process and analyze the trajectory and energy change data recorded in S3 to analyze the chemical reaction path, energy change and product distribution during the SEI formation process.

[0013] Furthermore, in S1, the format of the generated model file is PDB or XYZ.

[0014] Furthermore, in S2, the ReaxFF force field file is selected according to the characteristics of the battery material.

[0015] Furthermore, in S2, in the process of adjusting the force field parameters according to the simulation requirements, it is implemented based on ReaxFF-MD simulation, in which the basic assumption adopted is that the bond order between two atoms can be directly obtained from the interatomic distance.

[0016] Furthermore, in the ReaxFF-MD simulation, the energy of each atom is given by the following expression:

[0017] Esystem=Ebond+Eover+Eunder+Eval+Epen+Etors+Econj+EvdW+Ecoul

[0018] Among them, Ebond is the bond order related function describing the interatomic bonding energy;

[0019] Eover and Eunder are the corrected energies when the coordination number of an atom exceeds or falls below the normal bond order value derived from valence bond theory, respectively;

[0020] Eval and Etors are the energies associated with the valence angle and torsional deviation, respectively;

[0021] Epen is an additional penalty energy term used to reproduce the stability of a system with two double bonds sharing one atom;

[0022] Econj describes the contribution of conjugation effects to molecular energy;

[0023] EvdW includes repulsive interactions at short interatomic distances and attractive energies at long distances;

[0024] Ecoul refers to the Coulomb interaction energy of atomic pairs.

[0025] Furthermore, S3 specifically includes the following steps:

[0026] The molecular dynamics simulation is performed using the LAMMPS software package, wherein the initial sodium ion battery model adjusted in S2 is input into the LAMMPS software, and the corresponding simulation parameters are set;

[0027] After starting the simulation, the LAMMPS software calculates the interactions of all atoms and simulates their motion according to the defined force fields and boundary conditions;

[0028] While the simulation is running, the trajectories and energy changes of the atoms during the simulation are recorded.

[0029] Furthermore, S4 specifically includes the following steps:

[0030] The trajectory files and energy change data obtained by LAMMPS simulation in step S3 are sorted and preprocessed to meet the input requirements of ReacNetGenerator software. The preprocessing includes data cleaning, format conversion, and missing value processing;

[0031] After preprocessing, the data is imported into the ReacNetGenerator software, and the imported trajectory and energy data are analyzed using the ReacNetGenerator software to reveal the chemical reaction path, energy changes, and product distribution during the SEI formation process. The ReacNetGenerator software uses a graph theory-based method to treat each snapshot in the trajectory as an undirected graph, with atoms as nodes, chemical bonds as edges, and molecular fragments as subgraphs. Through graph traversal algorithms such as depth-first search, all species in the trajectory are identified and a reaction network is constructed to analyze the chemical reaction path, energy changes, and product distribution during the SEI formation process.

[0032] Furthermore, S4 also includes using visualization tools to display the chemical reaction pathways, energy changes, and product distribution simulation results obtained by analysis.

[0033] A second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for simulating the formation of a solid electrolyte interface in a sodium ion battery.

[0034] A third aspect of the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned method of simulating the formation of a solid electrolyte interface in a sodium ion battery.

[0035] Compared with the prior art, the present invention has the following technical advantages:

[0036] 1) The present invention provides a comprehensive simulation analysis of the formation process of the solid electrolyte interface (SEI) of sodium ion batteries by adopting a hybrid simulation method, combining ReaxFF-MD and ReacNetGenerator simulation methods. This multivariate simulation method can reveal in detail the microscopic mechanism of the SEI formation process from the perspective of electrochemical kinetics and charge balance, including the generation and evolution of gas products, inorganic products, and organic products. Compared with traditional single simulation methods, the present invention can provide more in-depth and accurate insights, thereby providing a more reliable theoretical basis for the design and optimization of battery materials.

[0037] 2) The present invention uses the graph-theory-based ReacNetGenerator method to analyze the formation of SEI, which is the first time in the study of sodium-ion batteries. This method can identify and analyze all chemical reaction species in the SEI formation process, construct a detailed reaction network, and provide a new perspective for understanding the formation mechanism of SEI. Through the application of depth-first search algorithm and hidden Markov model, the present invention can extract clear chemical reaction paths from complex trajectory data, improving the accuracy and reliability of simulation results.

[0038] 3) The simulation method of the present invention is easier to operate, can automatically adjust simulation parameters, monitor atomic trajectories and energy changes during the simulation in real time, and automatically record relevant data. This automated processing flow not only improves the efficiency of the simulation, but also reduces human errors, making the simulation results more stable and repeatable. By using visualization tools to display simulation results, the present invention makes complex simulation data easier to understand and interpret, further promoting the progress of sodium ion battery research. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The figure is a schematic flow chart of the method for simulating the formation of a solid electrolyte interface in a sodium ion battery in the present invention. DETAILED DESCRIPTION

[0040] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms and other features not clearly described in this technical solution are all considered to be common technical features disclosed in the prior art.

[0041] Example 1

[0042] This embodiment provides a method for simulating the formation of a solid electrolyte interface in a sodium ion battery. Figure 1 , the method comprises the following steps:

[0043] S1. Initial model generation, in which Packmol software is used to generate the initial sodium-ion battery model, define the size and boundary conditions of the simulation system, set the number and distribution of molecules, and ensure that there is no unreasonable contact;

[0044] In S1, the generated model file format is PDB or XYZ.

[0045] Step S1 of the present invention involves initial model generation, the principle of which is based on the use of Packmol software to construct an initial sodium ion battery model. Packmol is an open source molecular simulation tool that can place molecules randomly or in a specified distribution in a specific space according to user-defined sizes and boundary conditions. The key to this step is to ensure that the number and distribution of molecules in the model are consistent with both the chemical composition of the actual battery and the physical conditions of the simulation, while avoiding unreasonable contact between molecules, such as overlap or non-physical close contact, which will affect the accuracy of subsequent simulations.

[0046] After defining the dimensions and boundary conditions of the simulation system, Packmol generates a molecular model in the virtual space based on these parameters. These parameters include the geometry, size, molecular types and their relative positions of the battery model. By precisely controlling these factors, a virtual model that can represent the working environment of an actual sodium-ion battery can be created.

[0047] The generated model file format is PDB or XYZ, both of which are widely used molecular structure representation methods. The PDB format provides a standardized way to describe the coordinates of atoms, while the XYZ format is a simpler text file that contains the coordinates of atoms and atom type identifiers.

[0048] S2, force field parameter setting, wherein an appropriate ReaxFF force field file is selected and force field parameters are adjusted according to simulation requirements to adjust the initial sodium ion battery model generated in S1;

[0049] The ReaxFF force field file is selected according to the characteristics of the battery material. In the process of adjusting the force field parameters according to the simulation requirements, the simulation is based on ReaxFF-MD, in which the basic assumption adopted is that the bond order between two atoms can be directly obtained from the interatomic distance.

[0050] In ReaxFF-MD simulations, the energy of each atom is given by the following expression:

[0051] Esystem=Ebond+Eover+Eunder+Eval+Epen+Etors+Econj+EvdW+Ecoul

[0052] Among them, Ebond is the bond order related function describing the interatomic bonding energy;

[0053] Eover and Eunder are the corrected energies when the coordination number of an atom exceeds or falls below the normal bond order value derived from valence bond theory, respectively;

[0054] Eval and Etors are the energies associated with the valence angle and torsional deviation, respectively;

[0055] Epen is an additional penalty energy term used to reproduce the stability of a system with two double bonds sharing one atom;

[0056] Econj describes the contribution of conjugation effects to molecular energy;

[0057] EvdW includes repulsive interactions at short interatomic distances and attractive energies at long distances;

[0058] Ecoul refers to the Coulomb interaction energy of atomic pairs.

[0059] Step S2, i.e. setting the force field parameters, is a key step in the present invention for adjusting and optimizing the initial sodium-ion battery model. Its principle is based on the ReaxFF force field, which is a reactive force field that can describe and simulate the breaking and formation of chemical bonds. The core of the ReaxFF force field is that it can dynamically adjust the bond order according to the distance between atoms, thereby more accurately simulating the chemical reaction process.

[0060] In this step, you first select a ReaxFF force field file that matches the characteristics of the battery material. This is because different materials require different force field parameters to accurately describe their physical and chemical properties. After selecting the appropriate force field file, adjust the force field parameters according to the specific needs of the simulation. These adjustments can include parameters such as the strength, angle, and torsional potential of bonded and non-bonded interactions to ensure that the simulation results reflect the actual physical processes.

[0061] S3, simulation run, in which the initial sodium ion battery model adjusted in S2 is run using the LAMMPS command, and the trajectory and energy changes of atoms during the simulation are recorded;

[0062] S3 specifically includes the following steps:

[0063] The molecular dynamics simulation is performed using the LAMMPS software package, wherein the initial sodium ion battery model adjusted in S2 is input into the LAMMPS software, and the corresponding simulation parameters are set;

[0064] After starting the simulation, the LAMMPS software calculates the interactions of all atoms and simulates their motion according to the defined force fields and boundary conditions;

[0065] While the simulation is running, the trajectories and energy changes of the atoms during the simulation are recorded.

[0066] Step S3, i.e. simulation run, is the core step of implementing molecular dynamics simulation in the present invention. Its principle is based on using LAMMPS (Large-scale Atomic / Molecular Massively Parallel Simulator) software package to perform simulation of the initial sodium ion battery model adjusted by step S2. LAMMPS is a powerful molecular dynamics simulation tool that can handle complex systems involving a large number of atoms and is particularly suitable for performing long-time scale simulations.

[0067] In this step, the adjusted initial model in S2 is first input into the LAMMPS software. This model includes the atomic arrangement of battery materials, force field parameters, and other relevant physical and chemical properties. Next, set the simulation parameters, such as initial temperature, pressure, simulation time step, total simulation time, etc. These parameters define the physical conditions of the simulation and are crucial to ensuring the accuracy and reliability of the simulation results.

[0068] After starting the simulation, LAMMPS calculates the interactions between all atoms in the system based on the input force fields and boundary conditions. This includes bonded and non-bonded interactions between atoms, such as van der Waals forces and Coulomb interactions. LAMMPS uses efficient numerical methods and parallel computing technology to quickly and accurately calculate these interactions and simulate the movement and evolution of atoms.

[0069] While the simulation is running, LAMMPS records the trajectories and energy changes of atoms in real time. The atomic trajectories include information such as the position, velocity, and acceleration of each atom during the simulation, which can be used to analyze the dynamic behavior of atoms and the transport characteristics of materials. The energy change data reflects the thermodynamic state of the system during the simulation, such as changes in total energy, potential energy, and kinetic energy.

[0070] S4. Data analysis, in which ReacNetGenerator software is used to post-process and analyze the trajectory and energy change data recorded in S3 to analyze the chemical reaction path, energy change and product distribution during the SEI formation process.

[0071] S4 specifically includes the following steps:

[0072] The trajectory files and energy change data obtained by LAMMPS simulation in step S3 are sorted and preprocessed to meet the input requirements of ReacNetGenerator software. The preprocessing includes data cleaning, format conversion, and missing value processing;

[0073] After preprocessing, the data is imported into the ReacNetGenerator software, and the imported trajectory and energy data are analyzed using the ReacNetGenerator software to reveal the chemical reaction path, energy changes, and product distribution during the SEI formation process. The ReacNetGenerator software uses a graph theory-based method to treat each snapshot in the trajectory as an undirected graph, with atoms as nodes, chemical bonds as edges, and molecular fragments as subgraphs. Through graph traversal algorithms such as depth-first search, all species in the trajectory are identified and a reaction network is constructed to analyze the chemical reaction path, energy changes, and product distribution during the SEI formation process.

[0074] S4 also includes the use of visualization tools to display the chemical reaction pathways, energy changes, and product distribution simulation results obtained through analysis.

[0075] Example 2

[0076] This embodiment relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement a method for simulating the formation of a solid electrolyte interface (SEI) in a sodium ion battery. The computer program includes a series of instructions for guiding the processor to complete multiple steps including initial model generation, force field parameter setting, simulation operation, and data analysis.

[0077] In the initial model generation step, the program uses Packmol software to generate an initial sodium-ion battery model and defines the size and boundary conditions of the simulation system. Then, in the force field parameter setting step, the program selects the appropriate ReaxFF force field file and adjusts the force field parameters according to the simulation requirements to adapt to the characteristics of the initial sodium-ion battery model.

[0078] Then, in the simulation run step, the program uses the LAMMPS software package to perform molecular dynamics simulations, inputs the adjusted initial model into LAMMPS, and sets the corresponding simulation parameters. During the simulation, the program records the trajectory and energy changes of atoms to provide data support for subsequent analysis.

[0079] Finally, in the data analysis step, the program uses ReacNetGenerator software to post-process and analyze the recorded trajectory and energy change data to reveal the chemical reaction path, energy change and product distribution during the SEI formation process. In addition, the program also includes visualization tools to intuitively display the simulation results obtained from the analysis.

[0080] Through this computer program, researchers can simulate the SEI formation process of sodium-ion batteries on computers, thereby gaining a deeper understanding of its formation mechanism and providing a theoretical basis for the design and optimization of battery materials. The storage medium can be in any computer-readable format, such as a hard disk, CD, USB drive, etc., which is convenient for use and sharing on different computing platforms.

[0081] Example 3

[0082] This embodiment provides an innovative computer program product, which includes a specific computer program designed to simulate the process of forming a solid electrolyte interface (SEI) in a sodium ion battery. When the computer program is executed on a processor, it can fully implement a series of complex simulation steps, thereby deeply exploring the SEI formation mechanism of sodium ion batteries.

[0083] The computer program integrates multiple advanced simulation tools and algorithms, including Packmol for initial model generation, ReaxFF force field for describing atomic interactions, LAMMPS for performing molecular dynamics simulations, and ReacNetGenerator for analyzing simulation data and building reaction networks. The program is designed to allow users to customize simulation parameters, such as system size, molecular distribution, force field parameters, etc., to meet different research needs and battery material properties.

[0084] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A method for simulating a sodium ion battery to form a solid electrolyte interface, characterized in that: The method comprises the following steps: S1. Initial model generation, in which Packmol software is used to generate the initial sodium-ion battery model, define the size and boundary conditions of the simulation system, set the number and distribution of molecules, and ensure that there is no unreasonable contact; S2, force field parameter setting, wherein an appropriate ReaxFF force field file is selected and force field parameters are adjusted according to simulation requirements to adjust the initial sodium ion battery model generated in S1; S3, simulation run, in which the initial sodium ion battery model adjusted in S2 is run using the LAMMPS command, and the trajectory and energy changes of atoms during the simulation are recorded; S4. Data analysis, in which ReacNetGenerator software is used to post-process and analyze the trajectory and energy change data recorded in S3 to analyze the chemical reaction path, energy change and product distribution during the SEI formation process.

2. The method for forming a solid electrolyte interface by simulating a sodium ion battery according to claim 1, characterized in that: In S1, the generated model file format is PDB or XYZ.

3. The method for forming a solid electrolyte interface by simulating a sodium ion battery according to claim 1, characterized in that: In S2, the ReaxFF force field file is selected according to the characteristics of the battery material.

4. The method for forming a solid electrolyte interface by simulating a sodium ion battery according to claim 1, characterized in that: In S2, while adjusting the force field parameters according to the simulation requirements, the simulation was implemented based on ReaxFF-MD, in which the basic assumption adopted was that the bond order between two atoms can be directly obtained from the interatomic distance.

5. The method for forming a solid electrolyte interface by simulating a sodium ion battery according to claim 4, characterized in that: In ReaxFF-MD simulations, the energy of each atom is given by the following expression: Esystem=Ebond+Eover+Eunder+Eval+Epen+Etors+Econj+EvdW+Ecoul Among them, Ebond is the bond order related function describing the interatomic bonding energy; Eover and Eunder are the corrected energies when the coordination number of an atom exceeds or falls below the normal bond order value derived from valence bond theory, respectively; Eval and Etors are the energies associated with the valence angle and torsional deviation, respectively; Epen is an additional penalty energy term used to reproduce the stability of a system with two double bonds sharing one atom; Econj describes the contribution of conjugation effects to molecular energy; EvdW includes repulsive interactions at short interatomic distances and attractive energies at long distances; Ecoul refers to the Coulomb interaction energy of atomic pairs.

6. The method for forming a solid electrolyte interface by simulating a sodium ion battery according to claim 1, characterized in that: S3 specifically includes the following steps: The molecular dynamics simulation is performed using the LAMMPS software package, wherein the initial sodium ion battery model adjusted in S2 is input into the LAMMPS software, and the corresponding simulation parameters are set; After starting the simulation, the LAMMPS software calculates the interactions of all atoms and simulates their motion according to the defined force fields and boundary conditions; While the simulation is running, the trajectories and energy changes of the atoms during the simulation are recorded.

7. The method for forming a solid electrolyte interface by simulating a sodium ion battery according to claim 1, characterized in that: S4 specifically includes the following steps: The trajectory files and energy change data obtained by LAMMPS simulation in step S3 are sorted and preprocessed to meet the input requirements of ReacNetGenerator software. The preprocessing includes data cleaning, format conversion, and missing value processing; After preprocessing, the data is imported into the ReacNetGenerator software, and the imported trajectory and energy data are analyzed using the ReacNetGenerator software to reveal the chemical reaction path, energy changes, and product distribution during the SEI formation process. The ReacNetGenerator software uses a graph theory-based method to treat each snapshot in the trajectory as an undirected graph, with atoms as nodes, chemical bonds as edges, and molecular fragments as subgraphs. Through graph traversal algorithms such as depth-first search, all species in the trajectory are identified and a reaction network is constructed to analyze the chemical reaction path, energy changes, and product distribution during the SEI formation process.

8. The method for forming a solid electrolyte interface by simulating a sodium ion battery according to claim 1, characterized in that: S4 also includes the use of visualization tools to display the chemical reaction pathways, energy changes, and product distribution simulation results obtained through analysis.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for simulating the formation of a solid electrolyte interface in a sodium ion battery as described in any one of claims 1 to 8 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for simulating the formation of a solid electrolyte interface in a sodium ion battery as described in any one of claims 1 to 8 is implemented.

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