Artificial solid electrolyte interfacial film simulation method and device and computer equipment

Abstract

The invention relates to an artificial solid electrolyte interfacial film simulation method and device and computer equipment. The method comprises the following steps: based on crystal structure data, constructing an artificial solid electrolyte interface ASEI film composed of multiple materials, and carrying out convergence test processing on the ASEI film to determine parameter information of the ASEI film; constructing a heterojunction structure of the ASEI film according to the parameter information and predetermined surface performance of the ASEI film, and performing lattice matching treatment on the heterojunction structure to obtain an ASEI film interface model; performing lithium ion adsorption point location calculation processing on the ASEI membrane interface model, and determining the adsorption behavior of lithium ions on the surface of the ASEI membrane; and performing migration path simulation processing on the ASEI membrane interface model according to a plurality of adsorption energies corresponding to the adsorption behaviors, and determining a migration mechanism of lithium ions on the surface of the ASEI membrane. By adopting the method, the adsorption and migration behaviors of the lithium ions in the ASEI membrane can be analyzed.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment, and in particular to a method, device and computer equipment for simulating an artificial solid electrolyte interface membrane. Background Art

[0002] With the continuous development of the new energy vehicle industry, the demand for high-energy-density energy storage systems continues to grow. Lithium metal batteries, due to their high theoretical energy density, have attracted widespread attention and are considered one of the important directions of next-generation battery technology. In the practical application of lithium metal batteries, the artificial solid electrolyte interphase (ASEI) serves as a key intermediate layer between the electrolyte and the metal anode. Its performance greatly affects the cycle life and safety performance of the battery.

[0003] However, existing related research is still insufficient in terms of the adsorption and migration behavior of lithium ions in ASEI membranes, and there is still a lack of systematic and effective research methods. Therefore, there is an urgent need for a method that can be used to analyze the adsorption and migration behavior of lithium ions in ASEI membranes. Summary of the Invention

[0004] Based on this, it is necessary to provide an artificial solid electrolyte interface membrane simulation method, device and computer equipment that can analyze the adsorption and migration behavior of lithium ions in ASEI membranes to address the above technical problems.

[0005] In a first aspect, the present application provides a method for simulating an artificial solid electrolyte interface film, comprising:

[0006] Based on crystal structure data, artificial solid electrolyte interface (ASEI) membranes composed of various materials were constructed, and convergence tests were performed on the ASEI membranes to determine the parameter information of the ASEI membranes.

[0007] The heterojunction structure of the ASEI film is constructed according to the parameter information and the predetermined surface properties of the ASEI film, and the heterojunction structure is subjected to lattice matching processing to obtain an ASEI film interface model;

[0008] Calculate the lithium ion adsorption sites on the ASEI membrane interface model to determine the adsorption behavior of lithium ions on the ASEI membrane surface;

[0009] According to the multiple adsorption energies corresponding to the adsorption behavior, the migration path of the ASEI membrane interface model is simulated to determine the migration mechanism of lithium ions on the ASEI membrane surface.

[0010] In a second aspect, the present application also provides an artificial solid electrolyte interface membrane simulation device, comprising:

[0011] The parameter determination module is used to construct an artificial solid electrolyte interface (ASEI) membrane composed of various materials based on crystal structure data, and to perform convergence testing on the ASEI membrane to determine the parameter information of the ASEI membrane;

[0012] A model determination module is used to construct a heterojunction structure of the ASEI film based on parameter information and predetermined surface properties of the ASEI film, and to perform lattice matching processing on the heterojunction structure to obtain an ASEI film interface model;

[0013] Adsorption behavior determination module, used to calculate the lithium ion adsorption sites of the ASEI membrane interface model and determine the adsorption behavior of lithium ions on the ASEI membrane surface;

[0014] The migration mechanism determination module is used to simulate the migration path of the ASEI membrane interface model based on multiple adsorption energies corresponding to the adsorption behavior, and determine the migration mechanism of lithium ions on the ASEI membrane surface.

[0015] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0016] Based on crystal structure data, artificial solid electrolyte interface (ASEI) membranes composed of various materials were constructed, and convergence tests were performed on the ASEI membranes to determine the parameter information of the ASEI membranes.

[0017] The heterojunction structure of the ASEI film is constructed according to the parameter information and the predetermined surface properties of the ASEI film, and the heterojunction structure is subjected to lattice matching processing to obtain an ASEI film interface model;

[0018] Calculate the lithium ion adsorption sites on the ASEI membrane interface model to determine the adsorption behavior of lithium ions on the ASEI membrane surface;

[0019] According to the multiple adsorption energies corresponding to the adsorption behavior, the migration path of the ASEI membrane interface model is simulated to determine the migration mechanism of lithium ions on the ASEI membrane surface.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0021] Based on crystal structure data, artificial solid electrolyte interface (ASEI) membranes composed of various materials were constructed, and convergence tests were performed on the ASEI membranes to determine the parameter information of the ASEI membranes.

[0022] The heterojunction structure of the ASEI film is constructed according to the parameter information and the predetermined surface properties of the ASEI film, and the heterojunction structure is subjected to lattice matching processing to obtain an ASEI film interface model;

[0023] Calculate the lithium ion adsorption sites on the ASEI membrane interface model to determine the adsorption behavior of lithium ions on the ASEI membrane surface;

[0024] According to the multiple adsorption energies corresponding to the adsorption behavior, the migration path of the ASEI membrane interface model is simulated to determine the migration mechanism of lithium ions on the ASEI membrane surface.

[0025] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0026] Based on crystal structure data, artificial solid electrolyte interface (ASEI) membranes composed of various materials were constructed, and convergence tests were performed on the ASEI membranes to determine the parameter information of the ASEI membranes.

[0027] The heterojunction structure of the ASEI film is constructed according to the parameter information and the predetermined surface properties of the ASEI film, and the heterojunction structure is subjected to lattice matching processing to obtain an ASEI film interface model;

[0028] Calculate the lithium ion adsorption sites on the ASEI membrane interface model to determine the adsorption behavior of lithium ions on the ASEI membrane surface;

[0029] According to the multiple adsorption energies corresponding to the adsorption behavior, the migration path of the ASEI membrane interface model is simulated to determine the migration mechanism of lithium ions on the ASEI membrane surface.

[0030] The above-mentioned artificial solid electrolyte interface simulation method, apparatus, and computer equipment construct ASEI membranes with various material combinations based on crystal structure data. Convergence testing is then performed on this basis to ensure the rationality of the calculation parameters and the reliability of the results, providing an accurate basis for subsequent interface modeling. Furthermore, by combining the surface properties of the ASEI membrane to construct a heterojunction structure and perform lattice matching, the physical realism and structural stability of the interface model are effectively improved, making the constructed ASEI membrane interface model more similar to the structure of solid electrolyte membranes in actual battery operating environments. The calculation of lithium ion adsorption sites based on this foundation can accurately identify multiple stable adsorption configurations on the interface surface, reflecting the distribution behavior of lithium ions in the composite interface material. Simulating the migration paths corresponding to multiple adsorption energy pathways can reveal the possible low-energy channels of lithium ions within the interface and their migration mechanisms. This effectively deepens the understanding of the adsorption and migration behavior of lithium ions in ASEI membranes, provides quantifiable theoretical support for in-depth exploration of the relationship between interface structure and transport properties, and provides a scientific basis for the design and performance prediction of new artificial SEI membrane materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 is a diagram of the internal structure of a computer device in one embodiment;

[0033] Figure 2 A schematic flow chart of a method for simulating an artificial solid electrolyte interface membrane according to an embodiment;

[0034] Figure 3 A schematic flow chart of a method for simulating an artificial solid electrolyte interface membrane in another embodiment;

[0035] Figure 4 A schematic flow chart of a method for simulating an artificial solid electrolyte interface membrane in another embodiment;

[0036] Figure 5 A schematic flow chart of a method for simulating an artificial solid electrolyte interface membrane in another embodiment;

[0037] Figure 6 A schematic flow chart of a method for simulating an artificial solid electrolyte interface membrane in another embodiment;

[0038] Figure 7A schematic flow chart of a method for simulating an artificial solid electrolyte interface membrane in another embodiment;

[0039] Figure 8 A schematic flow chart of a method for simulating an artificial solid electrolyte interface membrane in another embodiment;

[0040] Figure 9 A schematic flow chart of a method for simulating an artificial solid electrolyte interface membrane in another embodiment;

[0041] Figure 10 The theoretical model diagram of Li2S-LiMg composite ASEI film;

[0042] Figure 11 is the stable site diagram of Li2S-LiMg(010);

[0043] Figure 12 Schematic diagram of the migration path of Li+ in the Li2S-LiMg composite ASEI film;

[0044] Figure 13 is the diffusion barrier of Li+ on the surface of Li2S-LiMg composite ASEI film;

[0045] Figure 14 1 is a structural block diagram of an artificial solid electrolyte interface membrane simulation device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0047] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 1As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data in the artificial solid electrolyte interface membrane simulation process. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an artificial solid electrolyte interface membrane simulation method is implemented.

[0048] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0049] In an exemplary embodiment, Figure 2 As shown, a method for simulating an artificial solid electrolyte interface film is provided, which is applied to Figure 1 The computer device in the embodiment is used as an example to illustrate the method, which includes the following steps 201 to 204. Among them:

[0050] Step 201 : constructing an artificial solid electrolyte interface (ASEI) membrane composed of multiple materials based on crystal structure data, and performing a convergence test on the ASEI membrane to determine parameter information of the ASEI membrane.

[0051] Crystal structure data refers to the arrangement of a material's atoms in the crystal lattice, typically including unit cell parameters, symmetry, atomic types, and their spatial positions. This data is typically derived from experimental measurements or material databases and is used to build an original simulation model of the material.

[0052] An artificial solid electrolyte interface (ASEI) is a solid interface membrane artificially designed and constructed between the lithium metal anode and the electrolyte. Its function is to inhibit lithium dendrite growth, improve interfacial stability and ionic conductivity, and provide a more optimized interfacial environment. Compared to naturally occurring SEI membranes, ASEI membranes have a more controllable composition and structure.

[0053] In the examples of this application, first, based on the crystal structure data of various constituent materials (such as solid electrolytes and functional metal compounds) in the target research system, an artificial solid electrolyte interface membrane model composed of multiple materials is constructed. The crystal structure data includes the unit cell parameters, space group information and atomic coordinate information of each material, which serves as the basic input for model establishment. During the model construction process, the original lattice characteristics of each material are retained, and a three-dimensional periodic unit cell structure is established through simulation software to form a multi-material combination system with true crystallographic symmetry.

[0054] After the model is initially established, the ASEI film model is subjected to a convergence test to determine the parameter information for subsequent simulation calculations. Specifically, multiple sets of candidate cutoff energies and Brillouin zone K-point grid combinations are set for each component material involved in the model (such as Li2S, LiMg, etc.), and static first-principles calculations are performed to analyze the trend of the total energy of the system changing with the calculated parameters. Through energy convergence judgment, after the cutoff energy and K-point density increase to a certain value, if the total energy change of the system is less than a preset threshold (such as 0.01eV), it is considered that the set of parameters meets the convergence requirements. Finally, the cutoff energy and K-point combination with lower computational cost under the energy convergence condition is selected as the target parameter, and the self-consistent field convergence criterion, ion force convergence threshold, atomic displacement tolerance and stress deviation limit are set in combination with the simulation accuracy requirements to form a parameter information set uniformly adopted in all subsequent simulation steps.

[0055] Step 202 : constructing a heterojunction structure of the ASEI film according to the parameter information and the predetermined surface properties of the ASEI film, and performing lattice matching processing on the heterojunction structure to obtain an ASEI film interface model.

[0056] Among them, parameter information refers to a set of computational control parameters determined through convergence testing for subsequent simulations, which usually include key numerical settings such as cutoff energy, K-point grid density, energy convergence accuracy, and self-consistent field tolerance.

[0057] Surface properties refer to the physical or electrochemical properties of a material surface, including but not limited to parameters such as surface energy and electron work function, which are used to determine surface stability and its ability to interact with ions.

[0058] A heterojunction structure is an interface structure formed by the crystal planes of two or more different materials. It is used to simulate the actual contact state between the composite ASEI film and lithium metal and electrolyte. Its construction requires lattice matching to reduce lattice mismatch and improve interface stability.

[0059] Lattice matching processing refers to adjusting the crystal plane sizes and angles of different materials in the heterojunction through lattice expansion, rotation or deformation, so as to achieve geometric coordination at the interface, reduce stress concentration and structural distortion, and ensure the physical rationality of the simulated interface structure.

[0060] The ASEI membrane interface model refers to a composite material interface model constructed on the basis of crystal structure modeling, lattice matching and surface performance analysis, which is used to simulate the interaction behavior between lithium ions and the interface, such as adsorption and migration processes.

[0061] In the examples of this application, a heterojunction structure is constructed based on parameters obtained from convergence testing and pre-determined ASEI film surface properties. These surface properties, including surface energy and electron work function calculated from crystal structure and surface state information, are used to determine the preferred orientation of each material's exposed crystal facets in interface construction. Based on this information, a combination of crystal faces with good thermodynamic stability and interface matching potential is selected. Lattice matching is then used to expand, rotate, or linearly stretch the lattices of the multiple materials to reduce the lattice mismatch to a reasonable range. The resulting structure forms a heterojunction ASEI film interface model with clear material boundaries and good geometric continuity.

[0062] Step 203 : Calculate the lithium ion adsorption sites on the ASEI membrane interface model to determine the adsorption behavior of lithium ions on the ASEI membrane surface.

[0063] Among them, adsorption behavior refers to the physical process in which lithium ions are attracted and fixed on the surface of the ASEI membrane. Its stability is usually characterized by adsorption energy. The lower the adsorption energy, the more stable the adsorption process.

[0064] In this example, based on the constructed and optimized ASEI membrane interface model, several potential adsorption sites were initially identified by analyzing the atomic density, symmetry, and surface atomic species in the interface region. These sites, including lattice vacancies, atomic bridges, and surface depressions, served as candidate targets for subsequent adsorption behavior analysis.

[0065] Then, a height range is set above each candidate adsorption site, and lithium ions are placed at different heights to perform rapid single-point static energy calculations without structural optimization, thereby determining the initial energy response at different positions. This process helps identify which adsorption sites have relatively low initial energies, thereby screening priority areas with greater adsorption potential and reducing the redundant overhead of subsequent high-precision calculations.

[0066] After initially identifying stable adsorption sites, the researchers further analyzed changes in the system's charge distribution before and after lithium ion adsorption. By comparing the electron distribution of the post-adsorption system with that of the original ASEI membrane model and isolated lithium ions, they observed the rearrangement of the surface electron cloud during the adsorption process. Significant accumulation or loss of electron density around an adsorption site indicates a significant electronic response to lithium ions and a strong adsorption tendency.

[0067] At the same time, the local work function distribution is calculated for each adsorption configuration. The local work function reflects the variation in the energy required to release electrons into the vacuum at different surface regions. Regions with lower work functions generally indicate greater potential for lithium ion electron exchange, meaning they are more likely to form a stable adsorption state. By overlaying the charge density variation with the local work function distribution, the lithium affinity and adsorption capacity of each adsorption site can be more intuitively determined.

[0068] Finally, the static energy test results, charge response characteristics and work function distribution are comprehensively considered to classify the adsorption behavior of all candidate adsorption sites. For example, stable adsorption sites are marked as preferred adsorption sites, those with weaker adsorption behavior are marked as secondary adsorption sites, and areas with no obvious adsorption trend are eliminated.

[0069] Step 204 : Based on the multiple adsorption energies corresponding to the adsorption behaviors, a migration path simulation process is performed on the ASEI membrane interface model to determine the migration mechanism of lithium ions on the ASEI membrane surface.

[0070] Adsorption energy represents the change in total system energy before and after lithium ions are adsorbed on the material surface. An adsorption energy less than zero indicates an exothermic, spontaneous adsorption process. A larger absolute value of the adsorption energy indicates a more stable adsorption site and a stronger lithium affinity.

[0071] Migration path simulation processing refers to constructing intermediate conformation points through interpolation based on known adsorption sites, and performing energy optimization and diffusion barrier calculations to obtain the migration kinetics of lithium ions on the interface surface.

[0072] The migration mechanism refers to the sum of information such as the microscopic path, transition state energy, and diffusion barrier of lithium ions diffusing on the surface of the ASEI membrane, which is used to reveal its interface transport performance and material design direction.

[0073] In an embodiment of the present application, based on the determination of the lithium ion adsorption behavior, the migration path simulation processing of the ASEI membrane interface model is further performed according to the multiple adsorption energy results corresponding to the adsorption behavior to determine the migration mechanism of lithium ions on the surface of the ASEI membrane. Specifically, the multiple adsorption energies are first sorted in descending order, and the two adsorption points with the lowest adsorption energy are selected as the starting point and end point of the migration path of the lithium ions. Between the starting point and the end point, a number of intermediate conformation points are evenly inserted using the interpolation method to construct a transition path for the lithium ion to migrate from one adsorption site to another adsorption site. Subsequently, structural relaxation calculations are performed on each intermediate conformation point to obtain its corresponding energy value. By analyzing the energy changes of the intermediate conformation points and calculating their energy difference relative to the starting adsorption point, the diffusion barrier and transition state energy distribution of the entire migration path are obtained. This process can reveal the energy barriers and path continuity experienced by lithium ions during the migration process on the ASEI membrane surface, and then analyze the relationship between the migration mechanism of lithium ions and the interface regulation ability from the atomic scale.

[0074] In this artificial solid electrolyte interface simulation method, ASEI membranes with various material combinations are constructed based on crystal structure data. Convergence testing is then performed to ensure the rationality of the calculation parameters and the reliability of the results, providing an accurate basis for subsequent interface modeling. Furthermore, a heterojunction structure is constructed based on the surface properties of the ASEI membrane and lattice matching is performed, effectively improving the physical realism and structural stability of the interface model, making the constructed ASEI membrane interface model more similar to the structure of solid electrolyte membranes in actual battery operating environments. The calculation of lithium ion adsorption sites based on this method accurately identifies multiple stable adsorption configurations on the interface surface, reflecting the distribution behavior of lithium ions in the composite interface material. Simulating the migration paths corresponding to multiple adsorption energy pathways reveals the possible low-energy channels and migration mechanisms of lithium ions within the interface, thereby effectively deepening the understanding of the adsorption and migration behavior of lithium ions in ASEI membranes. This provides quantifiable theoretical support for in-depth exploration of the relationship between interface structure and transport properties, and also provides a scientific basis for the design and performance prediction of new artificial SEI membrane materials.

[0075] In an exemplary embodiment, Figure 3 As shown, the above-mentioned "calculating the lithium ion adsorption sites on the ASEI membrane interface model to determine the adsorption behavior of lithium ions on the ASEI membrane surface" includes steps 301 to 304. Among them:

[0076] Step 301: determining a plurality of adsorption points based on ion arrangement information on the surface of the ASEI film interface model.

[0077] Ionic arrangement information refers to the geometric distribution of atoms or ions on the ASEI membrane surface in the crystal lattice, including ion type, coordinates, bond positions, symmetry, and surface void characteristics. This information is used to assist in identifying possible lithium ion adsorption sites.

[0078] Adsorption sites refer to locations on the ASEI membrane surface where lithium ions may be adsorbed. These are usually low-energy sites in the surface structure, such as bridge sites, vacancies, depressions, or high electron density regions around surface atoms.

[0079] In the present embodiment, a computer receives a pre-built and optimized ASEI membrane interface model and extracts information about its surface ion distribution. This information includes the types of surface-exposed atoms, their spatial distribution, bonding environment, and lattice symmetry. By analyzing this information, the computer identifies several candidate sites for lithium ion adsorption. These sites may be located at surface vacancies, bridge sites, or areas of high electron density, providing the initial basis for determining subsequent adsorption behavior.

[0080] Step 302 : For each adsorption point, lithium ions are placed at a preset height directly above the adsorption point to obtain an adsorption configuration.

[0081] The preset height refers to the initial set distance between the lithium ion and the surface in the vertical direction directly above the adsorption point when constructing the lithium ion adsorption configuration, which is usually set to In this embodiment of the present application, the preset height is set to

[0082] Adsorption configuration refers to the initial system structure formed after lithium ions are arranged at a specific adsorption point and maintained at a certain initial height. It is the input model for adsorption energy calculation and structural optimization.

[0083] In the embodiment of the present application, the computer device arranges lithium ions just above each candidate adsorption point according to a preset height parameter to construct an initial adsorption configuration. The preset height is usually set at The adsorption of lithium ions is controlled by the surface potential field to avoid structural overlap. Each adsorption configuration is used as an independent input for subsequent simulation processes.

[0084] Step 303 : performing structural optimization processing on the adsorption configuration based on density functional theory to determine the energy of the adsorption configuration.

[0085] Density Functional Theory (DFT) is a first-principles calculation method based on electron density rather than wave functions. It is used to predict the total energy, electronic structure, and interatomic interactions of a material system. This theory provides the theoretical basis for calculating adsorption configuration energies.

[0086] Structural optimization involves iteratively minimizing the total energy of the system and adjusting atomic positions until the forces acting on all atoms are below a specific threshold, resulting in a locally stable adsorption configuration. The optimized structure is then used to determine the subsequent energy and adsorption behavior.

[0087] The energy of the adsorption configuration refers to the total energy of the entire ASEI membrane model system after the lithium ion is at a specific adsorption point and the structure is optimized. It is used to calculate the adsorption energy and judge the stability of the adsorption behavior.

[0088] In the present embodiment, a computer device invokes a density functional theory-based structure optimization module to perform structural relaxation on each adsorption configuration. During this process, the total energy of the system is gradually minimized, and the atomic positions are continuously adjusted until the residual forces acting on all atoms fall below a set threshold, thereby obtaining a stable adsorption configuration and the corresponding total system energy. This total energy reflects the thermodynamic state of the ASEI membrane interface system after lithium ion adsorption.

[0089] Step 304 : Determine the adsorption behavior of all lithium ions on the ASEI membrane surface according to the energy of the adsorption configuration, the total energy of the ASEI membrane interface model before lithium ion adsorption, and the energy of a single lithium ion.

[0090] Among them, the total energy of the ASEI membrane interface model before the adsorption of lithium ions refers to the total energy of the ASEI membrane interface model constructed without the addition of lithium ions, which is used as a reference energy benchmark in the adsorption energy calculation.

[0091] The energy of a single lithium ion refers to the total energy of a lithium ion existing alone in a vacuum environment. It is usually obtained by constructing a sufficiently large vacuum box and performing static calculations. It is a necessary comparison value in adsorption energy evaluation.

[0092] In the present embodiment, after obtaining the energy of each adsorption configuration, the computer device further invokes an energy difference analysis module, combining two previously acquired reference values: the total energy of the ASEI membrane interface model when no lithium ions are adsorbed, and the energy of an isolated single lithium ion in a vacuum. Based on these three energy data, the device calculates the adsorption energy of each adsorption point and determines whether the adsorption process is thermodynamically spontaneous. A negative adsorption energy indicates that the adsorption process is exothermic and has adsorption stability; a larger absolute value of the adsorption energy indicates that the adsorption point has a stronger adsorption capacity for lithium ions.

[0093] In an exemplary embodiment, Figure 4 As shown, the above-mentioned "determining the adsorption behavior of all lithium ions on the ASEI membrane surface according to the energy of the adsorption configuration, the total energy of the ASEI membrane interface model before the adsorption of lithium ions, and the energy of a single lithium ion" includes steps 401 to 403. Among them:

[0094] Step 401 : determining the total energy of the ASEI membrane interface model after lithium ions are adsorbed at multiple adsorption points based on the energy of the adsorption configuration.

[0095] In this embodiment, lithium-ion adsorption configurations were constructed for multiple pre-determined adsorption sites. Each adsorption configuration was then structurally optimized to obtain the corresponding total system energy. The total energy of the optimized adsorption configuration reflects the system's stable state after lithium-ion adsorption at that specific location and is considered an energy reference for the ASEI membrane interface model after adsorption.

[0096] In step 402 , the total energy of the ASEI membrane interface model after lithium ion adsorption, the total energy of the ASEI membrane interface model before lithium ion adsorption, and the energy of a single lithium ion are substituted into an adsorption equation to obtain adsorption energy.

[0097] In this embodiment, a computer device invokes an energy processing module to read three key energy values: the energy of each adsorption configuration (i.e., the post-adsorption energy); the total energy of the ASEI membrane interface model before adsorption; and the energy of a single lithium ion in an isolated vacuum environment. These energy values ​​serve as input parameters for adsorption energy calculations.

[0098] The three energy values ​​above are input into the adsorption energy difference calculation logic to calculate the adsorption energy corresponding to each adsorption point. This adsorption energy quantifies the energy change after the lithium ion enters the ASEI membrane surface from the free state. The adsorption energy is usually negative, indicating that the adsorption process is exothermic and the adsorption behavior is driven by thermodynamic forces.

[0099] Step 403 : Determine the adsorption behavior of lithium ions on the surface of the ASEI membrane according to the adsorption energy and the preset energy threshold.

[0100] In the present embodiment, after obtaining the adsorption energy of all adsorption sites, each adsorption energy value is further compared with a preset energy threshold. This energy threshold is used to determine the spontaneity and stability of the adsorption behavior and is typically set based on material system experience or preliminary simulation tests. For example, if the adsorption energy is below this threshold, the adsorption site is considered to have a strong adsorption capacity for lithium ions.

[0101] Finally, the computer classifies the adsorption behavior of each adsorption site based on the comparison of the adsorption energy with the energy threshold, generating a set of adsorption behavior judgment results. This judgment includes whether the adsorption site has stable adsorption capacity, adsorption energy ranking information, and the spatial distribution of adsorption behavior on the ASEI membrane surface. This can be used for subsequent lithium ion migration path analysis or interface design optimization.

[0102] In some embodiments, the adsorption behavior of the lithium ions on the surface of the ASEI membrane is determined based on the adsorption energy and a preset energy threshold, including: when the adsorption energy is less than the preset energy threshold, determining that the lithium ions undergo spontaneous adsorption behavior on the surface of the ASEI membrane.

[0103] In a specific embodiment, the process of determining the adsorption energy u can be expressed as follows:

[0104]

[0105] In formula (1): E ads represents the adsorption energy, E con represents the total energy of the ASEI membrane interface model after lithium ion adsorption; E slab represents the total energy of the ASEI membrane interface model before lithium ion adsorption; Represents the energy of a single lithium ion; E ads <0 represents an exothermic process, indicating that adsorption can proceed spontaneously. |E ads The larger the |, the more stable the adsorption, the stronger the lithium affinity of the surface system, and the more conducive to the uniform deposition of lithium ions on the surface.

[0106] In an exemplary embodiment, Figure 5 As shown, the above-mentioned "simulating the migration path of the ASEI membrane interface model based on multiple adsorption energies corresponding to the adsorption behavior to determine the migration mechanism of lithium ions on the ASEI membrane surface" includes steps 501 to 504. Among them:

[0107] Step 501 : sorting multiple adsorption energies in descending order to obtain a sorting result.

[0108] In an embodiment of the present application, a computer device receives the adsorption energy values ​​of multiple adsorption points obtained from a previous adsorption calculation and sorts these adsorption energies in descending order. The purpose of this sorting is to quickly identify the most stable adsorption sites based on the relative magnitude of the adsorption energies. The lower the adsorption energy, the more stable the lithium ion adsorption at that location; while adsorption points with higher adsorption energies may only be transitional areas without sufficient anchoring capacity.

[0109] Step 502 : Determine the last two adsorption energies in the sorting result as the starting point and the ending point of the lithium ion migration path, respectively.

[0110] In this embodiment of the present application, the computer device selects the two adsorption points with the lowest adsorption energy values ​​from the sorted results as the starting and ending points of the lithium ion migration path. This selection method has a clear physical basis: on the one hand, these two points represent the locations where lithium ions are most likely to exist stably; on the other hand, the migration process of lithium ions between these two points is also most likely to occur during battery operation. After selecting the starting point and end point, these two spatial coordinates are used as the boundary nodes of the migration path, providing a reference for constructing the intermediate path.

[0111] Step 503: uniformly insert at least one intermediate constellation point between the starting point and the ending point.

[0112] In the embodiment of the present application, in order to simulate the continuous process of lithium ion migration from the starting point to the end point, the computer device uniformly inserts at least one intermediate conformation point on the path based on the three-dimensional spatial coordinates between the two points. The number of intermediate conformation points can be set according to the path length, and the initial atomic coordinates are usually generated using linear interpolation, elastic band method or other path generation algorithms. These intermediate conformation points do not represent stable adsorption states, but rather transition state configurations experienced during lithium ion migration, which are used to characterize the continuity, morphological changes and energy evolution of the diffusion path.

[0113] To enhance the physical plausibility of the intermediate conformations, the computer performs structural relaxation on each of them. During this process, the starting and ending configurations remain fixed, while only the degrees of freedom of the intermediate conformations are released. Structural optimization is then performed using first-principles or semi-empirical methods to achieve a local minimum energy state within the constraints of the path. This step eliminates problems such as unreasonable interatomic contacts and energy abrupt changes that may arise in the interpolated configurations, ensuring the overall energetic coherence and physical feasibility of the path.

[0114] Step 504 : determining the migration mechanism of lithium ions on the surface of the ASEI membrane based on the starting point, the ending point, and at least one intermediate conformation point.

[0115] In an embodiment of the present application, after relaxation is completed, the computer equipment extracts the total energy and draws the energy profile of the starting point, the end point and all the intermediate conformational points to form a complete migration path energy curve. By analyzing the energy difference between the highest energy point and the starting point in the curve, the diffusion barrier required for lithium ions to migrate along the path is calculated. If the barrier is within a reasonable range (such as <0.5eV), it can be determined that the path is a possible migration path. Further combined with the degree of configuration deformation of each node and the smoothness of the path, the reversibility and stability of the migration process can also be evaluated.

[0116] Finally, the computer integrates this energy data with the structural evolution results to produce a complete output of the lithium-ion migration mechanism. This output includes the locations of the migration path's starting and ending points, the structural evolution of intermediate conformations, the path's total energy profile, the diffusion barrier value, and transition state signatures. This migration mechanism not only provides a quantitative indicator of interfacial transport performance but also serves as an important reference for subsequent ASEI membrane structure optimization and material selection.

[0117] Through the coordinated execution of the above-mentioned multiple steps, the above-mentioned embodiment not only realizes the quantitative transition from adsorption behavior data to dynamic migration mechanism, but also provides a lithium ion migration path simulation process that is applicable to multi-material interface film systems and has repeatability and high precision.

[0118] In an exemplary embodiment, the migration mechanism includes an energy value and a diffusion barrier corresponding to at least one intermediate conformation point, such as Figure 6 As shown, the above-mentioned “determining the migration mechanism of lithium ions on the surface of the ASEI membrane based on the starting point, the ending point and at least one intermediate conformation point” includes steps 601 to 602. Among them:

[0119] Step 601 : For each intermediate conformation point, perform structural relaxation processing on the intermediate conformation point to obtain an energy value corresponding to the intermediate conformation point.

[0120] In the examples of the present application, a structural relaxation process is performed on each intermediate conformational point to obtain its corresponding stable conformation and energy value. During the structural relaxation process, density functional theory or other suitable calculation methods are used to optimize the atomic positions of the intermediate conformational point to achieve a local energy minimum state. After the relaxation calculation is completed, the energy value of the intermediate conformational point is obtained.

[0121] Step 602: The difference between the energy value corresponding to the intermediate conformation point and the adsorption energy corresponding to the starting point is determined as the diffusion barrier corresponding to the intermediate conformation point.

[0122] In the embodiment of the present application, the energy value corresponding to the intermediate conformation point is compared with the adsorption energy corresponding to the starting point, and the energy difference between the two is calculated. This energy difference is defined as the diffusion barrier corresponding to the intermediate conformation point. By comparing the diffusion barriers of all intermediate conformation points, the highest energy barrier in the diffusion path can be determined, and the key kinetic parameters of the diffusion process can be obtained. If it is necessary to reflect the diffusion path more accurately, path optimization techniques such as the elastic band method can be further used to refine and verify the path of the conformation point to ensure the reliability and physical rationality of the calculation results. Ultimately, combining the energy data to analyze the diffusion behavior will help to deeply understand the diffusion mechanism of the material surface or interface.

[0123] In an exemplary embodiment, the above parameter information includes at least the target cutoff energy and the target Brillouin K point. On this basis, Figure 7 As shown, the above-mentioned "performing a convergence test on the ASEI film to determine the parameter information of the ASEI film" includes steps 701 to 703. Among them:

[0124] Step 701 : For each material in the ASEI film, determine multiple sets of candidate cutoff energies and candidate Brillouin K points.

[0125] In this example, for each material in the ASEI film, multiple sets of candidate cutoff energies and candidate Brillouin zone K points were determined to construct a systematic parameter scanning scheme. These candidate cutoff energies covered a range of energies from low to high to ensure accurate capture of the convergence of the wave function in the material system; the candidate Brillouin K points included k-point grids of varying densities to reflect their impact on the accuracy of the system energy calculation.

[0126] Step 702 : Calculate the principle energy of each set of candidate cutoff energies and candidate Brillouin K points to obtain a trend change curve.

[0127] In this example, for each candidate cutoff energy and candidate Brillouin K-point combination, a total energy calculation was performed using first-principles methods to obtain the corresponding energy value. By sequentially adjusting the cutoff energy and K-point density, a series of energy data was obtained, and a trend curve of energy as a function of the cutoff energy and K-point density was plotted.

[0128] Step 703 : determining the energy change point of the trend change curve according to the preset energy threshold, determining the candidate cutoff energy point corresponding to the energy change point as the target cutoff energy, and determining the candidate Brillouin K point corresponding to the energy change point as the target Brillouin K point.

[0129] In an embodiment of the present application, in the trend analysis stage, according to a pre-set energy change threshold, a turning point or inflection point on the identification curve where the energy change rate significantly slows down and tends to be stable is identified. This energy change point indicates that the calculation accuracy has reached a certain stable level, and further improving the parameters will no longer significantly improve the calculation results. Based on this, the candidate cutoff energy corresponding to the change point is determined as the target cutoff energy, and the corresponding candidate Brillouin K point is determined as the target Brillouin K point. In order to ensure the scientific rationality of the target parameters, it is usually necessary to weigh the optimal parameter combination based on the consumption of computing resources, computing time and actual application requirements. In addition, repeated calculations or cross-validation can be used to ensure that the selected target cutoff energy and K point have good stability and applicability under different calculation conditions. Ultimately, the determined target cutoff energy and target Brillouin K point not only meet the energy convergence requirements, but also provide a reliable and efficient calculation basis for the subsequent simulation of the physicochemical properties of ASEI membrane materials, thereby ensuring the accuracy and repeatability of the calculation results.

[0130] In an exemplary embodiment, the surface properties include surface energy, based on which, Figure 8 As shown, the method further includes:

[0131] Step 801 : Determine the surface bottom area, unit cell energy, number of surface atoms, and total number of atoms in a unit cell of the ASEI film based on the crystal structure data of the ASEI film.

[0132] Step 802 , substituting the surface bottom area, the unit cell energy, the number of surface atoms, and the total number of atoms in the unit cell into the surface energy equation to calculate and obtain the surface energy of the ASEI film.

[0133] The unit cell is the smallest repeating unit in a crystal that fully reflects the periodicity and symmetry of the crystal structure. The unit cell energy refers to the energy possessed by a single unit cell, encompassing the kinetic energy and potential energy of the atoms within the unit cell, as well as the interaction energy between atoms. The unit cell energy is a fundamental parameter describing the energy state of a crystal and is crucial for understanding its stability, thermodynamic properties, and various physical processes.

[0134] The surface atom count refers to the number of atoms located on the surface of an ASEI film. Surface atoms possess unique physical and chemical properties due to their environment, which differs from that of bulk atoms. The surface atom count is a key parameter in calculating surface energy, reflecting the number of active sites on the surface and the degree of interaction between the surface and its external environment.

[0135] The total number of atoms in a unit cell refers to the total number of atoms contained within a single unit cell. This parameter reflects the chemical composition and structural complexity of a crystal and is important for calculating various physical properties of the crystal, such as density and specific heat. When calculating surface energy, the total number of atoms in a unit cell can be used for normalization to obtain comparable surface energy values.

[0136] Surface energy refers to the higher energy of a surface compared to the bulk phase due to the unsaturated force field of surface atoms. Surface energy reflects the activity and stability of a surface, and its magnitude is closely related to factors such as the crystal structure, the type and arrangement of surface atoms. Surface energy is a key parameter for measuring the surface properties of ASEI films, significantly influencing their interactions with other substances, such as wetting, adsorption, and chemical reactions.

[0137] In the embodiment of the present application, the theoretical model of the LiF-LiX (X = Mg, Ga, Zn) composite ASEI film is constructed using the CASTEP module in the Materials Studio software, and the calculation formula of the surface energy Esurf is shown in (2).

[0138]

[0139] In formula (2), A represents the bottom area of ​​the surface, Eslab represents the energy of the surface, Ebulk represents the energy of the unit cell, Nslab represents the number of atoms in the surface, and Nbulk represents the total number of atoms in the unit cell.

[0140] In an exemplary embodiment, the surface properties include electron work function, based on which, for example Figure 9 As shown, the method further includes:

[0141] Step 901, determining the crystal vacuum level and crystal Fermi level of the ASEI film based on the crystal structure data of the ASEI film;

[0142] Step 902: Determine the difference between the crystal vacuum level and the crystal Fermi level as the electron work function of the ASEI film.

[0143] The crystal vacuum level refers to the energy state of electrons in the vacuum outside the crystal. At absolute zero, electrons are in their lowest energy state. The energy level corresponding to the minimum energy required to move an electron from the interior of the crystal to the vacuum at infinity is the crystal vacuum level. It serves as a reference energy level, measuring the relative energy level of electrons in the crystal to the external vacuum environment.

[0144] The Fermi level is the chemical potential of electrons in a crystal, that is, the highest energy level that electrons can occupy at absolute zero. The probability of electrons occupying energy levels below this level is 1, while the probability of electrons occupying energy levels above this level is 0. The Fermi level reflects the distribution and energy state of electrons in a crystal and plays a key role in understanding the electrical and optical properties of crystals.

[0145] The electron work function refers to the minimum energy required to move an electron from the interior of a crystal to the vacuum outside the crystal surface. It is equal to the difference between the crystal's vacuum energy level and the crystal's Fermi level and is a key physical quantity that describes the ease with which electrons escape from the crystal surface. The electron work function influences electron transfer and interface properties when the crystal comes into contact with other materials, and is of great significance in fields such as semiconductor devices, optoelectronic materials, and batteries. For example, in batteries, the electron work function of the ASEI film affects the efficiency of charge transfer between it and the electrode, thereby affecting the battery's charge and discharge performance.

[0146] In the embodiment of the present application, the calculation formula of the electron work function W is shown in (3).

[0147] W=E vacuum -E fermi (3)

[0148] In formula (3), Evacuum represents the vacuum energy level of the crystal; Efermi represents the Fermi level.

[0149] In one embodiment, this embodiment takes the Li2S-LiMg simulation process as an example to illustrate the specific implementation of the present invention.

[0150] S1, Constructing a Theoretical Model of the Composite ASEI Membrane: Using density functional theory, we constructed a microscopic model of the solid electrolyte surface based on its crystal structure. By optimizing the crystal structure parameters, we obtained the lowest energy stable configuration, which served as the initial model for subsequent calculations.

[0151] S11, based on the crystal structure data, use material simulation software to construct the crystal model of the basic ASEI film and alloy.

[0152] The crystal structure of Li2S belongs to the cubic system and is a fluorite structure, in which Li + With 4 equivalent S 2- Combined to form a corner-sharing LiS4 tetrahedral structure. All Li-S bond lengths are S 2- In body-centered cubic geometry with 8 equivalent Li + Table 1 shows the relevant parameters of the Li2S crystal structure.

[0153] Table 1

[0154]

[0155] The Li2S unit cell has three typical low Miller index surfaces: (001), (110), and (111). To avoid artificial dipole interactions, a model with the same ends was established for each surface. The surface that satisfies the stoichiometric ratio, i.e., Li + With S 2- The number ratio is 2:1.

[0156] S12, through the calculation of surface energy and work function, the stable surface of each material is determined. The surface energy and electron work function are calculated. After calculation, Li2S(110) was selected for the subsequent composite ASEI film model construction, with a surface energy of 0.499J / m2 and a work function of 5.19eV.

[0157] S13 uses a heterojunction structure to construct a theoretical model of a composite ASEI film, and measures its matching degree using the lattice mismatch f. Table 2 shows the stable surface structure parameters. Table 3 shows the magnification factors of the surface structure parameters u and v at the Li2S / LiMg alloy interface and the lattice mismatch f.

[0158] Table 2

[0159]

[0160] Table 3

[0161]

[0162] The theoretical model of Li2S / LiMg composite ASEI film is as follows Figure 10 As shown. Figure 10 In the model, the material is composed of two materials with different crystal structures. The upper part is the cubic phase Li2S crystal structure, and the lower part is the hexagonal or cubic phase LiMg crystal structure. The two are constructed through the interface region to form a stable heterojunction.

[0163] The structure shown is a composite supercell model after lattice matching. To ensure geometric continuity and lattice coordination of the interface connection, the crystal plane orientations of Li2S and LiMg were uniformly selected and adjusted during the model construction process, resulting in a small lattice mismatch in the contact area. The structure is configured as a three-dimensional repeatable unit cell using periodic boundary conditions, in which the upper and lower crystal parts contact with a clear boundary interface, forming a complete interface film model.

[0164] On the upper and lower surface areas of the model, there are two layers with a thickness of approximately The vacuum layer is used to avoid mutual interference between different layers during the periodic calculation process and ensure that the physical boundary conditions in the subsequent adsorption behavior and migration path simulation are reasonable.

[0165] This theoretical model can be used for subsequent calculation of adsorption sites and analysis of lithium ion migration mechanisms based on density functional theory. It has good crystal integrity, controllability and simulation adaptability, and is suitable for the study of the microscopic interface structure of ASEI membranes composed of multiple materials.

[0166] S2. Calculation Condition Selection and Parameter Settings: Convergence tests were conducted on the cutoff energy and Brillouin zone K point for Li2S and LiMg. The system energy decreased with increasing cutoff energy. After the cutoff energy reached 700 eV, the energy change for all models was less than 0.01 eV, indicating convergence. Considering both accuracy and time, the K point was set to 5×5×5 and the cutoff energy to 700 eV.

[0167] The other parameters are selected with the highest accuracy, that is, the convergence accuracy of the self-consistent field is 5.0×10 -6 eV / atom, tolerance shift less than Relax until the force per atom does not exceed The stress deviation is less than 0.02GPa.

[0168] The Nose method was used to control the system temperature at 300 K. The run time was 10000 ps and the time step was 1 fs.

[0169] For each surface of the crystal model, the thickness is set to vacuum layer.

[0170] S3, Constructing the Interface Model of the Composite ASEI Membrane: A model of the interface between the composite ASEI membrane, the solid electrolyte, and the lithium metal anode was constructed. Lattice matching was used to model the heterojunction interface. To minimize the disparity between the length parameters of the various surface structures, the lattice was expanded. The heterojunction interface model was constructed using lattice matching. Table 4 shows the structural parameters of the surfaces involved in the composite interface. Table 5 shows the expansion factor of the surface structural parameters u and v at the ASEI membrane / Li interface and the lattice mismatch f.

[0171] Table 4

[0172]

[0173]

[0174] Table 5

[0175]

[0176] S4, Li+ adsorption site and migration path simulation.

[0177] S41 determines the stable sites on the surface by calculating the adsorption energy Eads. The model is corrected for DFT-D dispersion using the TS program. Li+ is pre-placed directly above each adsorption site, and the initial height is set to Then, through structural optimization, the ASEI membrane was optimized for Li+ adsorption. An Eads value less than 0 indicates an exothermic process, indicating that adsorption occurs spontaneously. A larger |Eads| value indicates more stable adsorption and a stronger lithiophilic surface system, which promotes more uniform Li+ deposition on the surface.

[0178] According to the common adsorption sites and surface structure optimization results, it was finally determined that Li2S-LiMg(010) has four stable sites such as Figure 11 shown. Figure 11 Used to identify and mark representative stable sites on the interfacial film surface for lithium ion adsorption. The structure shown is a top-down view perpendicular to the (010) crystal plane, illustrating the surface atomic arrangement of the Li2S and LiMg two-phase materials after forming the interface. The dashed border in the figure marks the boundary of the simulation area, and the interior is a periodic unit cell containing upper and lower lattice regions. The center is the interface connecting the Li2S and LiMg materials, i.e., the material transition zone within the composite ASEI film.

[0179] Figure 11 OTS, HL-1, HL-2, Middle, etc. marked with dotted circles are representative adsorption test points, which are distributed in different lattice regions: OTS (On Top of Sulfur): indicates the adsorption position directly above a surface sulfur atom in the interface film, which is used to test the adsorption capacity of typical non-metal sites with higher electronegativity for Li+; HL-1 and HL-2 are respectively located above representative lithium sites in the lattice regions on both sides, testing the adsorption behavior above metal ions in different parent phase regions; the Middle point is located near the geometric center of the interface connection area, indicating that Li+ is in a mixed environment at the junction of Li2S and LiMg, reflecting the influence of interface synergy on adsorption stability.

[0180] S42, using first-principles density functional theory calculations, provides two equilibrium structures. The calculation automatically inserts several intermediate conformations between them. The structures are then fully relaxed to determine the energy corresponding to each stable structure. The Li+ diffusion barrier, as well as the structure and energy of the transition state, are then calculated. Table 6 shows the adsorption energy and diffusion barrier of Li+ at different sites on the ASEI membrane surface.

[0181] Table 6

[0182]

[0183] The migration path of Li+ in Li2S-LiMg(010) is as follows Figure 12 As shown, the diffusion barrier is Figure 13 shown. Figure 12 The figure shows the migration path of Li+ in the Li2S-LiMg composite ASEI film. The spheres represent Li (lithium), S (sulfur), and Mg (magnesium).

[0184] Figure 12 Four different migration paths (Path1, Path2, Path3, and Path4) are marked in the figure: Path1: represents one migration direction of Li+ within the Li2S region. Path2: reflects the migration path of Li+ from the Li2S region to the LiMg region. Path3: represents the migration direction of Li+ within the LiMg region. Path4: shows another migration direction of Li+ within the LiMg region. Figure 13 The diffusion barrier of Li+ on the surface of Li2S-LiMg composite ASEI film is demonstrated. Figure 13In the graph, the horizontal axis is the "Diffusion Coordinate," which ranges from 0.0 to 1.0 and represents the different locations or stages of the Li+ diffusion process on the membrane surface. The vertical axis is the "Energy (eV)" (in electron volts), which ranges from -0.2 to 1.6 eV and measures the energy of the Li+ at different diffusion coordinate positions. Figure 13 There are four curves, representing different situations from top to bottom: OTS: Its energy change trend is to first rise to a peak (1.40eV) and then decrease. This peak represents the maximum potential barrier encountered by Li+ when diffusing along this path, which means that Li+ must cross this energy barrier to continue diffusing. Another curve: When the diffusion coordinate is at a certain position, the energy reaches 0.22eV. Compared with the OTS path, its potential barrier is significantly lower, indicating that Li+ diffuses relatively easily along this path. Another curve: The energy peak is 0.27eV, which is also an energy barrier in the Li+ diffusion process, but it is lower than the potential barrier of the OTS path. There is another curve: During the diffusion process, the energy reaches 0.15eV at a certain position, which is a relatively low energy peak among the four curves, indicating that the energy barrier that Li+ needs to overcome to diffuse along this path is the smallest.

[0185] Based on the same inventive concept, the present application also provides an artificial solid electrolyte interface membrane simulation device for implementing the artificial solid electrolyte interface membrane simulation method mentioned above. The solution to the problem provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more artificial solid electrolyte interface membrane simulation device embodiments provided below can be found in the above-mentioned limitations of the artificial solid electrolyte interface membrane simulation method, and will not be repeated here.

[0186] In an exemplary embodiment, Figure 14 As shown, an artificial solid electrolyte interface membrane simulation device is provided, including: a parameter determination module 1001, a model determination module 1002, an adsorption behavior determination module 1003 and a migration mechanism determination module 1004, wherein:

[0187] The parameter determination module 1001 is used to construct an artificial solid electrolyte interface (ASEI) membrane composed of multiple materials based on crystal structure data, and perform convergence testing on the ASEI membrane to determine parameter information of the ASEI membrane;

[0188] The model determination module 1002 is used to construct a heterojunction structure of the ASEI film based on the parameter information and the predetermined surface properties of the ASEI film, and perform lattice matching processing on the heterojunction structure to obtain an ASEI film interface model;

[0189] Adsorption behavior determination module 1003, used to calculate the lithium ion adsorption sites on the ASEI membrane interface model and determine the adsorption behavior of lithium ions on the ASEI membrane surface;

[0190] The migration mechanism determination module 1004 is used to perform a migration path simulation process on the ASEI membrane interface model according to multiple adsorption energies corresponding to the adsorption behavior, and determine the migration mechanism of lithium ions on the ASEI membrane surface.

[0191] Each module in the artificial solid electrolyte interface membrane simulation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0192] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0193] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0194] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0195] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile memory and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a programmable logic unit (PLC), a data processing logic unit based on quantum computing, an artificial intelligence (AI) processor, and the like.

[0196] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0197] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for simulating an artificial solid electrolyte interface film, characterized in that: The method comprises: Based on crystal structure data, an artificial solid electrolyte interface (ASEI) membrane composed of various materials is constructed, and convergence testing is performed on the ASEI membrane to determine parameter information of the ASEI membrane; constructing a heterojunction structure of the ASEI film according to the parameter information and the predetermined surface properties of the ASEI film, and performing lattice matching processing on the heterojunction structure to obtain an ASEI film interface model; Calculating the lithium ion adsorption sites on the ASEI membrane interface model to determine the adsorption behavior of lithium ions on the surface of the ASEI membrane; According to the multiple adsorption energies corresponding to the adsorption behavior, a migration path simulation process is performed on the ASEI membrane interface model to determine the migration mechanism of lithium ions on the surface of the ASEI membrane.

2. The method according to claim 1, characterized in that The calculating and processing of lithium ion adsorption sites on the ASEI membrane interface model to determine the adsorption behavior of lithium ions on the surface of the ASEI membrane includes: determining a plurality of adsorption points according to ion arrangement information on the surface of the ASEI membrane interface model; For each of the adsorption points, lithium ions are placed at a predetermined height directly above the adsorption point to obtain an adsorption configuration; performing structural optimization processing on the adsorption configuration based on density functional theory to determine the energy of the adsorption configuration; The adsorption behavior of all lithium ions on the surface of the ASEI membrane is determined based on the energy of the adsorption configuration, the total energy of the ASEI membrane interface model before the adsorption of lithium ions, and the energy of a single lithium ion.

3. The method according to claim 2, characterized in that Determining the adsorption behavior of all lithium ions on the surface of the ASEI membrane according to the energy of the adsorption configuration, the total energy of the ASEI membrane interface model before the adsorption of lithium ions, and the energy of a single lithium ion obtained in advance, includes: Determining the total energy of the ASEI membrane interface model after the lithium ions are adsorbed at the plurality of adsorption points according to the energy of the adsorption configuration; Substituting the total energy of the ASEI membrane interface model after lithium ion adsorption, the total energy of the ASEI membrane interface model before lithium ion adsorption, and the energy of a single lithium ion into the adsorption relationship to obtain adsorption energy; The adsorption behavior of the lithium ions on the surface of the ASEI membrane is determined according to the adsorption energy and a preset energy threshold.

4. The method according to claim 1, wherein The step of performing a migration path simulation on the ASEI membrane interface model based on the multiple adsorption energies corresponding to the adsorption behavior to determine the migration mechanism of lithium ions on the surface of the ASEI membrane includes: Sorting the plurality of adsorption energies in descending order to obtain a sorting result; Determine the last two adsorption energies in the sorting result as the starting point and the ending point of the lithium ion migration path respectively; At least one intermediate conformation point is uniformly inserted between the starting point and the ending point; The migration mechanism of lithium ions on the surface of the ASEI film is determined according to the starting point, the ending point and the at least one intermediate conformation point.

5. The method according to claim 4, characterized in that The migration mechanism includes an energy value and a diffusion barrier corresponding to at least one intermediate conformation point. Determining the migration mechanism of the lithium ions on the surface of the ASEI film based on the starting point, the ending point, and the at least one intermediate conformation point includes: For each of the intermediate conformation points, performing structural relaxation processing on the intermediate conformation point to obtain an energy value corresponding to the intermediate conformation point; The difference between the energy value corresponding to the intermediate conformation point and the adsorption energy corresponding to the starting point is determined as the diffusion barrier corresponding to the intermediate conformation point.

6. The method according to any one of claims 1 to 5, characterized in that The parameter information includes at least a target cutoff energy and a target Brillouin K point. The convergence test processing of the ASEI film to determine the parameter information of the ASEI film includes: For each material in the ASEI film, determining multiple sets of candidate cutoff energies and candidate Brillouin K points; Perform principle energy calculation on each set of candidate cutoff energies and candidate Brillouin K points to obtain trend change curves; The energy change point of the trend change curve is determined according to a preset energy threshold, and the candidate cutoff energy point corresponding to the energy change point is determined as the target cutoff energy, and the candidate Brillouin K point corresponding to the energy change point is determined as the target Brillouin K point.

7. The method according to any one of claims 1 to 5, characterized in that The surface property includes surface energy, and the method further comprises: Determining the surface bottom area, unit cell energy, number of surface atoms, and total number of atoms in a unit cell of the ASEI film according to the crystal structure data of the ASEI film; The surface bottom area, the single unit cell energy, the number of surface atoms and the total number of atoms in the single unit cell are substituted into the surface energy relationship to calculate the surface energy of the ASEI film.

8. The method according to any one of claims 1 to 5, characterized in that The surface property includes an electron work function, and the method further includes: determining the crystal vacuum energy level and the crystal Fermi energy level of the ASEI film according to the crystal structure data of the ASEI film; The difference between the crystal vacuum level and the crystal Fermi level is determined as the electron work function of the ASEI film.

9. An artificial solid electrolyte interface membrane simulation device, characterized in that: The device comprises: A parameter determination module is used to construct an artificial solid electrolyte interface (ASEI) membrane composed of multiple materials based on crystal structure data, and to perform convergence testing on the ASEI membrane to determine parameter information of the ASEI membrane; a model determination module, configured to construct a heterojunction structure of the ASEI film according to the parameter information and predetermined surface properties of the ASEI film, and perform lattice matching processing on the heterojunction structure to obtain an ASEI film interface model; An adsorption behavior determination module is used to calculate the lithium ion adsorption points of the ASEI membrane interface model to determine the adsorption behavior of lithium ions on the surface of the ASEI membrane; The migration mechanism determination module is used to perform a migration path simulation process on the ASEI membrane interface model according to the multiple adsorption energies corresponding to the adsorption behavior, and determine the migration mechanism of lithium ions on the surface of the ASEI membrane.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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