Method and apparatus for obtaining ion conductivity

Abstract

The application discloses an ion conductivity acquisition method and device, applied to the field of batteries, and can simulate the crystal structure and target structure of each inorganic component in the multiple inorganic components contained in the SEI film. The target structure can include at least one of an amorphous structure, a grain boundary structure and a pore interface structure. The pore interface structure is a structure obtained by taking the union set of the first coordinate set of atoms in the pore structure and the second coordinate set of atoms in the electrolyte. Therefore, not only the ion conductivity of the crystal structure can be acquired, but also the ion conductivity of the target structure can be acquired. Thus, the acquisition efficiency of the ion conductivity of different types of structures is improved, and since the ion conductivity of multiple dimensions of the SEI film can be acquired, the acquired ion conductivity is more comprehensive.

Description

Technical Field

[0001] This application relates to the field of batteries, specifically to a method and apparatus for obtaining ionic conductivity. Background Technology

[0002] In lithium-ion batteries, the solid electrolyte interphase (SEI) film is a core component affecting battery cycle performance, rate performance, and safety. Its ionic conductivity directly determines the migration rate of lithium ions between the electrode and the electrolyte. Since SEI films include different types of structures, their ionic conductivity is affected by the ionic conductivity of different structures. Therefore, optimizing the SEI film can be approached by studying the ionic conductivity of the structures.

[0003] In related technologies, researchers typically obtain the ionic conductivity of various types of structures in the SEI membrane through experimental measurement, which results in low efficiency in obtaining ionic conductivity. Summary of the Invention

[0004] In view of the above problems, this application provides a method and apparatus for obtaining ionic conductivity, the technical solution including:

[0005] On the one hand, a method for obtaining ionic conductivity is provided, the method comprising:

[0006] For each inorganic component among the various inorganic components contained in the solid electrolyte interface membrane, simulate the crystal structure and target structure of the inorganic component. The target structure includes at least one of amorphous structure, grain boundary structure and pore interface structure. The pore interface structure is the structure obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte.

[0007] For each structure in the crystal structure and the target structure, obtain the atomic trajectory of the structure and the first curve of velocity change over time. Based on the atomic trajectory and the first curve of velocity change, obtain the second curve of force on the atoms in the structure over time, as well as the energy of the structure. Based on the second curve of velocity change and the energy, obtain the ionic conductivity of the structure.

[0008] Optionally, there can be multiple pore interface structures, and the pore parameters of the pore structures corresponding to any two pore interface structures are different. The pore parameters include at least one of pore size and pore shape. This allows for the evaluation of the influence of pores of different shapes and / or sizes on ion transport behavior.

[0009] Optionally, after obtaining the ionic conductivity of the structure, the method further includes:

[0010] Obtain the structure with the highest ionic conductivity from at least two ionic conductivity values;

[0011] The manufacturing parameters of lithium batteries are adjusted based on the structure with the highest ionic conductivity to increase the proportion of the structure with the highest ionic conductivity in the solid electrolyte membrane.

[0012] Optionally, a second curve showing the force exerted on atoms in the structure over time, and the energy of the structure, are obtained based on the atomic trajectories and the first curve, including:

[0013] By inputting the atomic trajectory and the first change curve into the potential function model, we obtain the second change curve of the force on the atoms in the structure over time, as well as the energy of the structure, output by the potential function model.

[0014] The second change curve and the energy of the structure can be quickly obtained through the potential function model.

[0015] Optionally, the method also includes:

[0016] Obtain the first dataset, which includes the first training parameters and the first label parameters for each sample crystal structure in multiple sample crystal structures;

[0017] Obtain a second dataset, which includes the second training parameters and second label parameters for each sample target structure in multiple sample target structures. The sample target structure includes at least one of sample amorphous structure, sample grain boundary structure and sample pore interface structure. The sample pore interface structure is the structure obtained by taking the union of the first sample coordinate set of sample atoms in the sample pore structure and the second sample coordinate set of sample atoms in the sample electrolyte.

[0018] The potential function model is obtained by training on the first and second datasets;

[0019] Each of the first training parameters and the second training parameters includes: the trajectory of the sample atom and the first change curve of the sample velocity over time; each of the first label parameters and the second label parameters includes: the second change curve of the force on the sample atom over time and the sample energy.

[0020] Because SEI films contain diverse inorganic components and have varied structures, typically including crystalline, amorphous, grain boundary, and pore interface structures, this application generates sample crystalline structures and target structures based on the main inorganic components of the SEI film. During the training of the potential function model, in addition to using a first dataset containing multiple sample crystalline structures, a second dataset containing at least one of sample amorphous structures, sample grain boundary structures, and sample pore interface structures is also used. This ensures that the trained potential function model accurately describes the SEI film.

[0021] Optionally, before retrieving the first dataset, the method may also include:

[0022] Obtain inorganic components from multiple samples;

[0023] Obtain the crystal structure of inorganic components from various samples;

[0024] When the target structure of the sample includes the sample amorphous structure, obtain the sample amorphous structure of various inorganic components;

[0025] When the target sample structure includes a sample grain boundary structure, a predetermined number of first sample inorganic components are obtained from multiple sample inorganic components, the same-phase grain boundary of each first sample inorganic component is obtained, and the different-phase grain boundary composed of each pair of different first sample inorganic components is obtained, so as to obtain multiple sample grain boundary structures.

[0026] When the target sample structure includes a sample pore interface structure, multiple sample pore structures are obtained based on at least one sample crystal structure, and the first set of sample coordinates of sample atoms in each sample pore structure is combined with the second set of sample coordinates of sample atoms in the sample electrolyte to obtain multiple sample pore interface structures.

[0027] Each sample crystal structure corresponds to at least two sample pore structures. The pore parameters of any two sample pore structures are different. The pore parameters include at least one of the sample shape and sample size of the pore.

[0028] Optionally, the potential function model is trained on the first and second datasets, including:

[0029] The initial potential function model is trained using the first dataset to obtain the first potential function model;

[0030] The first potential function model was trained using the second dataset to obtain the potential function model.

[0031] Optionally, when the target structure of the sample includes the sample amorphous structure, the sample grain boundary structure and the sample pore interface structure, the second dataset includes the first subset, the second subset and the third subset. The first subset includes training parameters and label parameters for multiple sample amorphous structures, the second subset includes training parameters and label parameters for multiple sample grain boundary structures, and the third subset includes training parameters and label parameters for multiple sample pore interface structures.

[0032] The first potential function model is trained using the second dataset to obtain the potential function model, including:

[0033] The first potential function model is trained using the first subset of the dataset to obtain the second potential function model;

[0034] The second potential function model is trained using the second subset of the dataset to obtain the third potential function model;

[0035] The third potential function model is trained using the third subset of the dataset to obtain the potential function model.

[0036] Optionally, grain boundary structures include in-phase grain boundaries of inorganic components and out-of-phase grain boundaries composed of inorganic components and other inorganic components.

[0037] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements a method for obtaining ionic conductivity as described above.

[0038] In another aspect, a computer device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method for obtaining the ionic conductivity described above.

[0039] Furthermore, an apparatus for obtaining ionic conductivity is provided, comprising:

[0040] The first acquisition module is used to simulate the crystal structure and target structure of each inorganic component among the various inorganic components contained in the solid electrolyte interface membrane. The target structure includes at least one of amorphous structure, grain boundary structure and pore interface structure. The pore interface structure is the structure obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte.

[0041] The second acquisition module is used to acquire the atomic trajectory of the structure and the first velocity change curve over time for each structure in the crystal structure and the target structure, acquire the second force change curve of the atoms in the structure over time based on the atomic trajectory and the first change curve, and the energy of the structure, and acquire the ionic conductivity of the structure based on the second change curve and the energy.

[0042] In summary, this invention provides a method and apparatus for obtaining ionic conductivity. For each inorganic component in an SEI film, the method can simulate its crystal structure and target structure. The target structure can include at least one of amorphous structures, grain boundary structures, and pore interface structures. The pore interface structure is obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte. For each structure, both crystal and target, a computer device can obtain a second curve showing the force exerted on atoms in the structure over time, as well as the energy of the structure, based on the atomic trajectories and a first change curve. The ionic conductivity of the structure is then obtained based on the second change curve and the energy. Since various structures can be simulated and their ionic conductivity calculated using a computer, the efficiency of obtaining ionic conductivity for different types of structures in the SIE film is improved compared to related technologies. Furthermore, since not only the ionic conductivity of crystal structures but also that of target structures can be obtained, the ionic conductivity of the SEI film can be obtained in multiple dimensions, ensuring a more comprehensive acquisition of ionic conductivity. Attached Figure Description

[0043] Figure 1 This is a flowchart of a method for obtaining ionic conductivity provided in an embodiment of this application;

[0044] Figure 2 This is a flowchart of another method for obtaining ionic conductivity provided in an embodiment of this application;

[0045] Figure 3 This application provides a schematic diagram of the diffusion coefficients of various inorganic components at room temperature, extrapolated using the Arrhenius formula;

[0046] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application;

[0047] Figure 5 This is a block diagram of an ionic conductivity acquisition device provided in an embodiment of this application;

[0048] Figure 6 This is a block diagram of another ionic conductivity acquisition device provided in the embodiments of this application. Detailed Implementation

[0049] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0051] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0052] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0053] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0054] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0055] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0056] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0057] In lithium-ion batteries, the SEI film is a core component that affects the battery's cycle performance, rate performance, and safety. Its ionic conductivity directly determines the migration rate of lithium ions between the electrode and the electrolyte.

[0058] The inventors of this application have discovered that since SEI films include different types of structures, the ionic conductivity of the SEI film is affected by the ionic conductivity of different types of structures. Therefore, the SEI film can be optimized by studying the ionic conductivity of the structures.

[0059] However, in related technologies, researchers usually obtain the ionic conductivity of various types of structures in the SEI membrane through experimental measurement, which results in low efficiency in obtaining ionic conductivity.

[0060] This application provides a method for obtaining ionic conductivity. In this method, for each inorganic component among the various inorganic components contained in the SEI membrane, the crystal structure and target structure of that inorganic component can be simulated. The target structure can include at least one of an amorphous structure, a grain boundary structure, and a pore interface structure. The pore interface structure is obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte. For each structure, both the crystal structure and the target structure, a computer device can obtain a second curve showing the force exerted on atoms in the structure over time, as well as the energy of the structure, based on the atomic trajectories and a first change curve of that structure. The ionic conductivity of the structure is then obtained based on the second change curve and the energy. Since various structures can be simulated and their ionic conductivity calculated by computer, the efficiency of obtaining the ionic conductivity of different types of structures in the SIE membrane is improved compared to related technologies. Furthermore, since not only the ionic conductivity of the crystal structure but also that of the target structure can be obtained, the ionic conductivity of the SEI membrane in multiple dimensions can be obtained, ensuring a more comprehensive acquisition of the ionic conductivity.

[0061] Figure 1 This is a flowchart of a method for obtaining ionic conductivity provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes:

[0062] Step 101: For each inorganic component in the SEI film, simulate the crystal structure of the inorganic component and the target structure.

[0063] For each of the various inorganic components contained in the SEI membrane, the computer equipment can use simulation software to simulate the crystal structure and target structure of that inorganic component, or it can use molecular dynamics (MD) to simulate the crystal structure and target structure of the inorganic component. The target structure can include at least one of an amorphous structure, a grain boundary structure, and a pore interface structure. The pore interface structure is obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte. The simulation software can be COMSOL software.

[0064] For example, the SEI membrane can contain 17 inorganic components, such as lithium fluoride (LiF), lithium oxide (Li2O), lithium carbonate (Li2CO3), etc.

[0065] Step 102: For each structure in the crystal structure and the target structure, obtain the ionic conductivity of the structure.

[0066] For each crystal structure and target structure, the computer device can acquire the target parameters of the structure and characterize the ionic conductivity of the structure based on these target parameters. These target parameters may include the diffusion barrier, and ionic conductivity is negatively correlated with the diffusion barrier.

[0067] Optionally, the computer device can acquire the atomic trajectories of the structure and a first curve showing the velocity over time, acquire a second curve showing the force on the atoms in the structure over time based on the atomic trajectories and the first curve, acquire the energy of the structure, and acquire the ionic conductivity of the structure based on the second curve and the energy.

[0068] Since the structure of SEI films is highly variable, typically including crystalline structure, amorphous structure, grain boundary structure, and pore interface structure, for each inorganic component involved in the SEI film, by obtaining the ionic conductivity of the crystal structure and target structure of the inorganic component, the ionic conductivity of the SEI film in multiple dimensions can be obtained, ensuring that the obtained ionic conductivity is more comprehensive.

[0069] In summary, this application provides a method for obtaining ionic conductivity. For each inorganic component in an SEI film, the method can simulate its crystal structure and target structure. The target structure can include at least one of amorphous structure, grain boundary structure, and pore interface structure. The pore interface structure is obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte. For each structure, both crystal and target, a computer device can obtain a second curve showing the force exerted on atoms in the structure over time, as well as the energy of the structure, based on the atomic trajectories and a first change curve. The ionic conductivity of the structure is then obtained based on the second change curve and the energy. Since various structures can be simulated and their ionic conductivity calculated using a computer, the efficiency of obtaining ionic conductivity for different types of structures in the SIE film is improved compared to related technologies. Furthermore, since not only the ionic conductivity of the crystal structure but also that of the target structure can be obtained, the ionic conductivity of the SEI film can be obtained in multiple dimensions, ensuring a more comprehensive acquisition of ionic conductivity.

[0070] Figure 2 This is a flowchart of another method for obtaining ionic conductivity provided in an embodiment of this application, such as... Figure 2 As shown, the method includes:

[0071] Step 201: Obtain various inorganic components of the sample, and obtain the sample crystal structure and target structure of each inorganic component.

[0072] In this embodiment, the computer device can simulate multiple inorganic components of the sample in simulation software, or the computer device can use MD to simulate multiple inorganic components of the sample. These multiple inorganic components can be one of the 17 main inorganic components that may appear in the SEI membrane, such as LiF, Li2O, and Li2CO3. The simulation software can be Comsol software.

[0073] In one optional implementation of this application, for each sample inorganic component, the computer device can obtain the sample crystal structure of that inorganic component from a database. Alternatively, the computer device can construct the crystal structure of the sample inorganic component based on density functional theory (DFT). In another optional implementation of this application, the computer device can obtain only the sample crystal structures of a first number of target sample inorganic components.

[0074] The target structure of the sample may include at least one of the following: amorphous structure, grain boundary structure, and pore interface structure. The pore interface structure is the union of the first set of sample coordinates of the sample atoms in the pore structure and the second set of sample coordinates of the sample atoms in the electrolyte. The electrolyte may include ethylene carbonate (EC), methyl ethyl carbonate (EMC), and lithium hexafluorophosphate (LiPF6).

[0075] When the target sample structure includes an amorphous structure, the computer device can acquire the amorphous structures of various inorganic components of the sample. Alternatively, the computer device can acquire only the amorphous structures of a first number of target inorganic components.

[0076] The computer device can pre-store a first quantity, for example, 2. Since LiF and Li2O account for a significant proportion in the SEI film, the first quantity of inorganic components in the target sample can include LiF and Li2O.

[0077] The disorder of grain boundary structures makes it easier for amorphous structures to appear in the grain boundary region, and the migration direction of ions within the grain boundary is not restricted by crystal orientation, exhibiting isotropic behavior and improving ionic conductivity. Furthermore, the disorder of grain boundary structures significantly reduces the energy barrier (potential energy surface) that ions need to overcome during migration. This promotes rapid ion migration in the grain boundary region, ultimately improving the overall ionic conductivity of the material.

[0078] Amorphous structures (i.e., non-crystalline structures) allow for the formation of more randomly distributed lithium-ion vacancies within the material. Furthermore, the absence of fixed lattice constraints in amorphous structures makes lithium-ion migration paths more flexible, unconstrained by strict lattice jumping. Their isotropic nature also ensures that the difficulty of lithium-ion migration in different directions tends to be consistent. In addition, the randomness of the atomic arrangement makes the potential energy surface encountered by lithium-ion migration more gradual, lowering the energy barrier and facilitating smoother lithium-ion migration.

[0079] Computer equipment can use molecular dynamics initial structure construction software (such as Packmol software) to generate the initial amorphous structure of the sample's inorganic components. Then, ab initio molecular dynamics (AIMD) simulations are performed on the initial amorphous structure to sequentially perform high-temperature melting and quenching treatments, obtaining a reference amorphous structure. For example, the high-temperature melting temperature can be 2000 Kelvin (K), and the quenching rate can be 10¹² Kelvin per second (K / s).

[0080] Next, the stability of the reference amorphous structure can be verified using the radial distribution function (RDF) and structure factor. If the stability of the reference amorphous structure meets the preset requirements, it is used as the sample amorphous structure for the inorganic component of the sample. If the stability of the reference amorphous structure does not meet the preset requirements, the process of generating the sample amorphous structure for the inorganic component of the sample can be repeated until a stable sample amorphous structure is obtained.

[0081] When the target sample structure includes a sample grain boundary structure, the computer device can obtain a preset number of first sample inorganic components from a variety of sample inorganic components, obtain the sample homophase grain boundaries of each first sample inorganic component, and obtain the sample heterophase grain boundaries composed of every two different first sample inorganic components, so as to obtain multiple sample grain boundary structures.

[0082] For each first sample inorganic component, the computer device can combine the first and second crystal planes of the first sample inorganic component to obtain the sample-phase grain boundaries of the first sample inorganic component. The first and second crystal planes can both be any crystal plane of the first sample inorganic component, and the first and second crystal planes are different.

[0083] For two different first sample inorganic components, the computer device can combine the most stable crystal planes of the two first sample inorganic components to obtain the sample heterogeneous grain boundaries. The spacing between the two most stable crystal plane combinations is approximately 1 angstrom to 2 angstroms.

[0084] The computer device can pre-store a preset quantity; for example, the preset quantity can be 2. Since LiF and Li₂O constitute a significant proportion of the SEI film, the first sample inorganic component can include LiF and Li₂O. The sample homophase grain boundaries can include homophase grain boundaries composed of LiF-LiF and Li₂O-Li₂O. The sample heterophase grain boundaries can include heterophase grain boundaries composed of LiF-Li₂O.

[0085] When the target sample structure includes a sample pore interface structure, the computer device can obtain multiple sample pore structures based on at least one sample crystal structure, and take the union of the first set of sample coordinates of sample atoms in each sample pore structure with the second set of sample coordinates of sample atoms in the sample electrolyte to obtain multiple sample pore interface structures.

[0086] The number of at least one sample crystal structure can be a second quantity; for example, the at least one sample crystal structure can be a LiF crystal structure.

[0087] For each sample crystal structure, computer equipment can use simulation software to extract sample channels within the sample crystal structure, thereby generating the sample pore structure. Then, the union of the first set of sample coordinates of the sample atoms in the sample pore structure and the second set of sample coordinates of the sample atoms in the sample electrolyte can be taken to obtain the sample pore interface structure.

[0088] Each sample crystal structure corresponds to at least two sample pore structures. The pore parameters of any two of these pore structures are different. The pore parameters may include at least one of the sample shape and sample size. For example, the size of the pore may be 1 nanometer (nm) × 1 nm.

[0089] In this embodiment, since each sample atom in the sample crystal structure has coordinates, the computer device removes sample atoms within the sample size at the sample position in the sample crystal structure, thereby creating sample channels in the sample crystal structure. The processed sample crystal structure then serves as the sample porosity structure. The sample position can be any location within the sample crystal structure.

[0090] Furthermore, since each sample atom in the sample electrolyte also has coordinates, the computer device can take the union of the first set of sample coordinates of multiple sample atoms in the sample pore structure with the second set of sample coordinates of multiple sample atoms in the sample electrolyte to obtain a third set of sample coordinates. The structure formed by all atoms corresponding to the third set of sample coordinates is then determined as the sample pore interface structure. This is how the sample pore interface structure is obtained.

[0091] Step 202: Obtain the first dataset and the second dataset, and train the first dataset and the second dataset to obtain the potential function model.

[0092] After acquiring the sample crystal structure and sample target structure of various inorganic components, the computer equipment can acquire the first dataset and the second dataset, train the first dataset and the second dataset to obtain the potential function model.

[0093] The first dataset may include the first training parameters and the first label parameters of each sample crystal structure in the multiple sample crystal structures, and the second dataset may include the second training parameters and the second label parameters of each sample target structure in the multiple sample target structures.

[0094] Each training parameter in the first and second training parameters may include: the sample atom trajectory, and a first variation curve of the sample velocity over time. Each label parameter in the first and second label parameters may include: a second variation curve of the force on the sample atom over time, and the sample energy.

[0095] For each sample crystal structure, the computer device can perform AIMD simulations on the sample crystal structure at various first sample temperatures to obtain the sample atom trajectories, the sample velocity changing with time, the sample atom force changing with time, and the sample energy of the sample crystal structure at each first sample temperature, thereby obtaining the first dataset.

[0096] Among them, the ensemble for AIMD simulation of the sample crystal structure is a microcanonical (NVE) ensemble with a total time of 50 picoseconds (ps) to 100 ps and a time step of 0.5 femtoseconds (fs) to 1 fs. The multiple first sample temperatures can include 5 temperatures: 300K, 700K, 1000K, 1500K and 2000K.

[0097] When the target sample structure includes an amorphous sample structure, the second dataset includes the first subset, which may include training parameters and label parameters for multiple amorphous sample structures. For each amorphous sample structure, a computer device can perform AIMD simulations on the amorphous sample structure at various second sample temperatures to obtain the sample atom trajectories, the first variation curve of sample velocity over time, the second variation curve of sample atom force over time, and the sample energy, thereby obtaining the first subset.

[0098] The AIMD simulation ensemble for the amorphous structure of the sample is a canonical (NVT) ensemble, with a total time duration of 50 ps to 100 ps and a time step of 0.5 fs to 1 fs. The multiple second sample temperatures can include four temperatures: 300 K, 700 K, 1000 K, and 1300 K.

[0099] When the target sample structure includes a sample grain boundary structure, the second dataset may include a second subset, which may include training parameters and label parameters for multiple sample grain boundary structures. For each sample grain boundary structure, the computer device can perform AIMD simulations on the sample grain boundary structure at various second sample temperatures to obtain the sample atom trajectories, the first variation curve of sample velocity over time, the second variation curve of sample atom force over time, and the sample energy, thereby obtaining the second subset.

[0100] The AIMD simulation ensemble for the grain boundary structure of the samples is the NVT ensemble, with a total duration of 50 ps to 100 ps and a time step of 0.5 fs to 1 fs. The multiple second sample temperatures can include four temperatures: 300 K, 700 K, 1000 K, and 1300 K.

[0101] When the target sample structure includes a sample pore interface structure, the second dataset may also include a third subset, which may include training parameters and label parameters for multiple sample pore interface structures. For each sample pore interface structure, the computer device can perform AIMD simulations on the sample pore interface structure at various second sample temperatures to obtain the sample atom trajectories, the first variation curve of sample velocity over time, the second variation curve of sample atom force over time, and the sample energy of the sample pore interface structure at the second sample temperature, thereby obtaining the first subset.

[0102] Among them, the ensemble for AIMD simulation of the sample amorphous structure is a canonical (NVT) ensemble, with a total time length of 50ps to 100ps and a time step of 0.5fs to 1fs.

[0103] The process of training a potential function model using a computer device on a first dataset and a second dataset may include steps A1 and A2:

[0104] Step A1: Train the initial potential function model using the first dataset to obtain the first potential function model.

[0105] The computer device can train the initial potential function model using the first dataset to obtain the first potential function model. The computer device can pre-store the initial potential function model, which can be a neural network potential function, such as a matrix contracted expansions (MACE) potential function or a deep potential molecular dynamics (DeepMD) potential function.

[0106] In some embodiments, the computer device may divide the first dataset into a training set and a test set to train the initial potential function model and use leave-one-out cross-validation to evaluate the generalization ability of the initial potential function model.

[0107] Step A2: Train the first potential function model using the second dataset to obtain the potential function model.

[0108] The computer device can use a second dataset to train the first potential function model, thereby obtaining the potential function model. Specifically, the computer device divides the second dataset into a training set and a test set to train the first potential function model, and can evaluate the generalization ability of the first potential function model through leave-one-out cross-validation.

[0109] When the target sample structure includes at least one of the sample amorphous structure, sample grain boundary structure, and sample pore interface structure, the computer device trains the subset of data corresponding to the at least one sample structure to obtain the potential function model.

[0110] Optionally, when the target structure of the sample includes the sample amorphous structure, the sample grain boundary structure, and the sample pore interface structure, the computer device can use the first subset of the dataset to train the first potential function model to obtain the second potential function model, and use the second subset of the dataset to train the second potential function model to obtain the third potential function model, and use the third subset of the dataset to train the third potential function model to obtain the potential function model.

[0111] Because SEI films contain diverse inorganic components and have varied structures, typically including crystalline, amorphous, grain boundary, and pore interface structures, the pore structure within these interfaces affects the ionic conductivity of the SEI film. In this application, based on the main inorganic components of the SEI film, sample crystalline structures and target structures are generated. During the training of the potential function model, in addition to using a first dataset containing multiple sample crystalline structures, a second dataset containing at least one of sample amorphous structures, sample grain boundary structures, and sample pore interface structures is also used. This ensures that the trained potential function model accurately describes the SEI film.

[0112] Step 203: For each inorganic component contained in the SEI film, obtain the crystal structure and target structure of the inorganic component.

[0113] For each inorganic component in the SEI membrane, the computer can use simulation software to simulate the crystal structure and target structure of that inorganic component, or use MD simulation to obtain the crystal structure and target structure of that inorganic component. For example, the SEI membrane may contain 17 inorganic components, such as lithium fluoride (LiF), lithium oxide (Li₂O), and lithium carbonate (Li₂CO₃). The simulation software can be Comsol software.

[0114] The target structure may include at least one of an amorphous structure, a grain boundary structure, and a pore interface structure. The grain boundary structure may include in-phase grain boundaries composed of different crystal planes of the inorganic component, and out-of-phase grain boundaries composed of the inorganic component and other inorganic components. The pore interface structure may be the structure obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte. Other inorganic components are inorganic components other than the target inorganic component among a plurality of inorganic components.

[0115] In the embodiments of this application, each inorganic component may have one or more pore interface structures. When there are multiple pore interface structures, the pore parameters of the pore structures corresponding to any two pore interface structures are different. The pore parameters may include at least one of pore size and pore shape.

[0116] For crystal structures of inorganic components, computer equipment can create channels within the crystal structure, generating a porous structure. Then, the union of the first set of coordinates of the atoms in the porous structure and the second set of coordinates of the atoms in the electrolyte can be used to obtain the pore interface structure.

[0117] Since each atom in a crystal structure has coordinates, a computer device removes atoms within the size of a channel at a predetermined position in the crystal structure, thereby creating a channel within the crystal structure. The processed crystal structure then becomes a porous structure. The computer device can pre-store these predetermined positions.

[0118] Furthermore, since each atom in the electrolyte also has coordinates, the computer device can take the union of the first set of coordinates of multiple atoms in the pore structure with the second set of coordinates of multiple atoms in the electrolyte to obtain a third set of coordinates. The structure formed by all atoms corresponding to the third set of coordinates is then determined as the pore interface structure. This is how the pore interface structure is obtained.

[0119] The composition of the sample electrolyte used may differ for different types of lithium batteries. For each type of lithium battery, the composition of the sample electrolyte in step 201 is the same as that in step 203. This application embodiment does not limit the composition of the sample electrolyte in step 201 or the composition of the electrolyte in step 203.

[0120] Furthermore, since the embodiments of this application are based on the study of the ionic conductivity of the SEI membrane structure to optimize the SEI membrane, and the ionic conductivity of the pore structure affects the ionic conductivity of the SEI membrane, the concentration of the sample electrolyte in step 201 of the embodiments of this application can be any concentration, and the concentration of the electrolyte in step 203 can also be any concentration. The embodiments of this application do not limit this.

[0121] Step 204: For each structure in the crystal structure and the target structure, obtain the atomic trajectory of the structure and the first curve of the velocity change over time.

[0122] For crystal structures, computer equipment can perform AIMD simulations on the crystal structure at various first temperatures to obtain the atomic trajectories of the crystal structure at each first temperature, as well as the first velocity variation curve over time. The ensemble used for AIMD simulation of the crystal structure can be the NVE ensemble.

[0123] For each structure in the target structure, the computer equipment can perform AIMD simulations on the target structure at various second temperatures, obtaining the atomic trajectories of the target structure at each second temperature, as well as the first velocity variation curve over time. The ensemble used for the AIMD simulation of the target structure can be the NVT ensemble.

[0124] Step 205: Input the atomic trajectory and the first change curve into the potential function model to obtain the second change curve of the force on the atoms in the structure over time, as well as the energy of the structure, output by the potential function model.

[0125] After acquiring the atomic trajectories of the structure and the first velocity change curve over time, the computer device can input the atomic trajectories and the first change curve into the potential function model to obtain the second change curve of the force on the atoms in the structure over time, as well as the energy of the structure, output by the potential function model.

[0126] Optionally, the computer device can input the atomic trajectories and first variation curves of the crystal structure at various first temperatures into the potential function model to obtain the second variation curve of the atomic forces on the crystal structure at various first temperatures as well as the energy of the structure, which is output by the potential function model.

[0127] In another alternative implementation of this application, a computer device can perform AIMD simulations on the atomic trajectories and first change curves of the crystal structure at various first temperatures to obtain a second change curve of the forces acting on the atoms of the crystal structure over time at various first temperatures, as well as the energy of the structure.

[0128] The computer device can input the atomic trajectories and first change curves of the target structure at various second temperatures into the potential function model to obtain the second change curves of the atomic forces on the target structure at various second temperatures, as well as the energy of the structure, output by the potential function model.

[0129] In another optional implementation of this application, the computer device can perform AIMD simulation on the target structure at each second temperature to obtain a second curve showing the change of atomic forces over time at each second temperature, as well as the energy of the structure.

[0130] Similarly, molecular dynamics simulations at different temperatures can help estimate ion conduction rates at room temperature more accurately in later stages.

[0131] Step 206: Based on the second change curve and energy, obtain the ionic conductivity of the structure.

[0132] Computer equipment can perform molecular dynamics (MD) simulations on the second change curve and energy to obtain target parameters, which are then used to characterize ionic conductivity. These target parameters may include the diffusion coefficient or diffusion barrier; ionic conductivity is positively correlated with the diffusion coefficient and negatively correlated with the diffusion barrier.

[0133] In the case of a crystalline, amorphous, or grain boundary structure, for each temperature, the computer device can perform MD simulations on the second variation curve and energy at that temperature to obtain the target parameters at that temperature. This temperature can be either a first temperature or a second temperature.

[0134] Optionally, the computer equipment can perform MD simulations on the second variation curves and energy at various temperatures to obtain the diffusion coefficients at each temperature. Then, the diffusion coefficients at multiple temperatures are input into the Arrhenius equation to obtain a fitted straight line. Based on the fitted straight line, the diffusion energy barrier or diffusion coefficient at room temperature can be obtained.

[0135] Table 1 shows the diffusion coefficients of different inorganic components in Simulation 1 and Simulation 2. Simulation 1 refers to crystalline structures, and Simulation 2 refers to amorphous structures. Referring to Table 1, it can be seen that the diffusion coefficient of Li₂O with a crystalline structure is 6.07 × 10⁻⁶. -15 .

[0136] Table 1

[0137]

[0138] Figure 3 This application provides a schematic diagram illustrating the extrapolation of diffusion coefficients of various inorganic components at room temperature using the Arrhenius formula, as shown in the embodiments below. Figure 3 As shown in the diagram, the horizontal axis represents the outdoor temperature, in Kelvin (K). -1 1000 / T (K -1 The value represents the reciprocal of the temperature multiplied by 1000. The vertical axis represents the diffusion coefficient, with units of square meters per second (m²). 2 / s). The schematic diagram includes the diffusion coefficient of Li₂O as a function of temperature, Li 1.998 The diffusion coefficient of O varies with temperature, and Li 1.98 The diffusion coefficient of O as a function of temperature, and the diffusion coefficient of Li 1.8 The curve showing the diffusion coefficient of O as a function of temperature.

[0139] In the case of a porous interface structure, the computer device can input the second curves of the atomic force over time at a target second temperature from multiple second temperatures, along with the structure's energy, into a potential of mean force (PMF) algorithm to obtain the diffusion energy barrier at the target second temperature. The target second temperature can be room temperature.

[0140] In some embodiments of this application, for each structure in the crystal structure and the target structure, a computer device can process the structure using a molecular dynamics force field to obtain atomic trajectories and a first variation curve. After obtaining a second variation curve of the atomic force over time and the energy of the structure based on the atomic trajectories and the first variation curve, the second variation curve of the atomic force over time and the energy of the structure are input into the nudged elastic band (NEB) algorithm to obtain the diffusion barrier of the structure.

[0141] Step 207: Obtain the structure with the highest ionic conductivity from at least two ionic conductivity values.

[0142] After obtaining the ionic conductivity of at least two structures, the computer device can select the structure with the highest ionic conductivity from the at least two ionic conductivity values, and can adjust the manufacturing parameters of the lithium battery based on the structure with the highest ionic conductivity to increase the proportion of the structure with the highest ionic conductivity in the SEI film.

[0143] When the target parameters include the diffusion barrier, the computer device can use the structure with the smallest diffusion barrier as the structure with the highest ionic conductivity.

[0144] For example, among the crystal structure, amorphous structure, grain boundary structure and pore interface structure of inorganic components, the grain boundary structure has the smallest diffusion energy barrier. Therefore, computer equipment can regard the grain boundary structure as the structure with the highest ionic conductivity of the inorganic component.

[0145] Because SEI films have diverse structures, typically including crystalline, amorphous, grain boundary, and pore interface structures, for each of the various inorganic components involved in the SEI film, the structure with the highest ionic conductivity is selected from the crystalline and target structures of that inorganic component. Subsequently, the manufacturing parameters of the lithium battery can be adjusted to increase the proportion of the structure with the highest ionic conductivity in the SEI film of the lithium battery, thereby optimizing the SEI film.

[0146] Assuming the inorganic component is Li2O, if the crystal structure of this inorganic component and the structure with the highest ionic conductivity in the target structure are crystal structures, then the manufacturing parameters of the lithium battery can be the content of the Li component. During the production of lithium batteries, the Li component in the lithium battery can be increased, thereby resulting in a greater number of crystal structures in the generated SEI film.

[0147] If the inorganic component has a crystal structure and the structure with the highest ionic conductivity in the target structure is an amorphous structure, then the manufacturing parameters for the lithium battery can be the formation temperature. During the formation of the SEI film, the formation temperature can be increased, resulting in a higher proportion of amorphous structures in the generated SEI film. For example, the formation temperature during the SEI film formation process could be 60°C.

[0148] If the structure with the highest ionic conductivity in the crystal structure and target structure of the inorganic component is a grain boundary structure, then the manufacturing parameters of the lithium battery can be the number of types of inorganic components. In the process of producing lithium batteries, increasing the number of types of inorganic components in the lithium battery can result in a greater number of grain boundary structures in the generated SEI film.

[0149] If the structure with the highest ionic conductivity in the crystal structure and target structure of the inorganic component is a porous interface structure, then the manufacturing parameters of the lithium battery can be the amount of organic components or the charge and discharge parameters during the SEI film formation process. During the SEI film formation process, the amount of organic components in the lithium battery can be increased, or the charge and discharge parameters during the SEI film formation process can be adjusted so that the porous interface structure in the generated SEI film has more porous structures.

[0150] Furthermore, the manufacturing parameters of the lithium battery can be adjusted based on the structure with the highest ionic conductivity of only one inorganic component, or the manufacturing parameters of the lithium battery can be adjusted by combining the structures with the highest ionic conductivity of at least two inorganic components, thereby achieving the purpose of optimizing the SEI film. This application does not limit this.

[0151] It should be noted that, at the current stage, the optimization of SEI membranes by researchers is still in the exploratory phase. The applicant of this application has discovered that the ionic conductivity of various structures of inorganic components may affect the ionic conductivity of the SEI membrane. Therefore, the embodiments of this application only consider optimizing the SEI membrane from the perspective of the ionic conductivity of various structures of inorganic components. Other factors that may affect the ionic conductivity of the SEI membrane (such as organic components) are not considered in the embodiments of this application.

[0152] In another optional implementation of this application, after acquiring the ionic conductivity of at least two structures, the computer device can also display the ionic conductivity of at least two structures. This allows researchers to determine the ionic conductivity of at least two structures of each inorganic component without conducting experiments, thus improving the efficiency of acquiring the ionic conductivity of each structure of the inorganic component.

[0153] In this embodiment, the ion transport capacity from the electrolytic liquid phase to the pore interior is calculated by obtaining the ionic conductivity of the pore interface structure. Furthermore, since the pore parameters of the pore structures corresponding to multiple pore interface structures are different, this embodiment can evaluate the influence of pore shape and / or pore size on ion transport behavior.

[0154] In the embodiments of this application, the second change curves and energies of various structures are obtained through the potential function model, thereby obtaining the ionic conductivity of various structures. This simplifies the process of obtaining the ionic conductivity of the target structure in the SEI film and accelerates the prediction of the ionic conductivity of the target structure.

[0155] In summary, this application provides a method for obtaining ionic conductivity. For each inorganic component in an SEI film, the method can simulate its crystal structure and target structure. The target structure can include at least one of amorphous structure, grain boundary structure, and pore interface structure. The pore interface structure is obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte. For each structure, both crystal and target, a computer device can obtain a second curve showing the force exerted on atoms in the structure over time, as well as the energy of the structure, based on the atomic trajectories and a first change curve. The ionic conductivity of the structure is then obtained based on the second change curve and the energy. Since various structures can be simulated and their ionic conductivity calculated using a computer, the efficiency of obtaining ionic conductivity for different types of structures in the SIE film is improved compared to related technologies. Furthermore, since not only the ionic conductivity of the crystal structure but also that of the target structure can be obtained, the ionic conductivity of the SEI film can be obtained in multiple dimensions, ensuring a more comprehensive acquisition of ionic conductivity.

[0156] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for obtaining ionic conductivity described in the above embodiments.

[0157] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, such as... Figure 4 As shown, the computer device 40 may include: a memory 401, a processor 402, and a computer program stored in the memory 401 and executable on the processor 402. When the processor 402 executes the computer program, it implements the method for obtaining ionic conductivity as described in the above embodiments.

[0158] Figure 5 This is a block diagram of an ionic conductivity acquisition device provided in an embodiment of this application, as shown below. Figure 5 As shown, the device includes:

[0159] The first acquisition module 501 is used to simulate the crystal structure and target structure of each inorganic component among the various inorganic components contained in the solid electrolyte interface membrane. The target structure includes at least one of amorphous structure, grain boundary structure and pore interface structure. The pore interface structure is the structure obtained by taking the union of the first coordinate set of atoms in the pore structure and the second coordinate set of atoms in the electrolyte.

[0160] The second acquisition module 502 is used to acquire the atomic trajectory of the structure and the first change curve of the velocity over time for each structure in the crystal structure and the target structure, acquire the second change curve of the force on the atoms in the structure over time based on the atomic trajectory and the first change curve, and the energy of the structure, and acquire the ionic conductivity of the structure based on the second change curve and the energy.

[0161] Optionally, there are multiple pore interface structures, and the pore parameters of the pore structures corresponding to any two pore interface structures are different. The pore parameters include at least one of pore size and pore shape.

[0162] Optional, see reference Figure 6 The device also includes a third acquisition module 503 for acquiring the structure with the highest ionic conductivity from at least two ionic conductivityes;

[0163] The manufacturing parameters of lithium batteries are adjusted based on the structure with the highest ionic conductivity to increase the proportion of the structure with the highest ionic conductivity in the SEI film.

[0164] Optionally, the second acquisition module 502 is used for:

[0165] By inputting the atomic trajectory and the first change curve into the potential function model, we obtain the second change curve of the force on the atoms in the structure over time, as well as the energy of the structure, output by the potential function model.

[0166] Optionally, the second acquisition module 502 is used for:

[0167] Obtain the first dataset, which includes the first training parameters and the first label parameters for each sample crystal structure in multiple sample crystal structures;

[0168] Obtain a second dataset, which includes the second training parameters and second label parameters for each sample target structure in multiple sample target structures. The sample target structure includes at least one of sample amorphous structure and sample pore interface structure. The sample pore interface structure is the structure obtained by taking the union of the first sample coordinate set of sample atoms in the sample pore structure and the second sample coordinate set of sample atoms in the sample electrolyte.

[0169] The potential function model is obtained by training on the first and second datasets;

[0170] Each of the first training parameters and the second training parameters includes: the trajectory of the sample atom and the first change curve of the sample velocity over time; each of the first label parameters and the second label parameters includes: the second change curve of the force on the sample atom over time and the sample energy.

[0171] Optionally, the second acquisition module 502 is used for:

[0172] Before obtaining the first dataset, obtain the inorganic components of multiple samples;

[0173] Obtain the crystal structure of inorganic components from various samples;

[0174] When the target structure of the sample includes the sample amorphous structure, obtain the sample amorphous structure of various inorganic components;

[0175] When the target sample structure includes a sample grain boundary structure, a predetermined number of first sample inorganic components are obtained from multiple sample inorganic components, the same-phase grain boundary of each first sample inorganic component is obtained, and the different-phase grain boundary composed of each pair of different first sample inorganic components is obtained, so as to obtain multiple sample grain boundary structures.

[0176] When the target sample structure includes a sample pore interface structure, multiple sample pore structures are obtained based on at least one sample crystal structure, and the first set of sample coordinates of sample atoms in each sample pore structure is combined with the second set of sample coordinates of sample atoms in the sample electrolyte to obtain multiple sample pore interface structures.

[0177] Each sample crystal structure corresponds to at least two sample pore structures. The pore parameters of any two sample pore structures are different. The pore parameters include at least one of the sample shape and sample size of the pore.

[0178] Optionally, the second acquisition module 502 is used for:

[0179] The initial potential function model is trained using the first dataset to obtain the first potential function model;

[0180] The first potential function model was trained using the second dataset to obtain the potential function model.

[0181] Optionally, when the target sample structure includes amorphous structure, grain boundary structure, and pore interface structure, the second dataset includes a first subset, a second subset, and a third subset. The first subset includes training parameters and label parameters for multiple amorphous structures, the second subset includes training parameters and label parameters for multiple grain boundary structures, and the third subset includes training parameters and label parameters for multiple pore interface structures. The second acquisition module 502 is used for:

[0182] The first potential function model is trained using the first subset of the dataset to obtain the second potential function model;

[0183] The second potential function model is trained using the second subset of the dataset to obtain the third potential function model;

[0184] The third potential function model is trained using the aforementioned third subset of data to obtain the potential function model.

[0185] Optionally, the grain boundary structure includes in-phase grain boundaries of inorganic components, as well as heterogeneous grain boundaries composed of inorganic components and other inorganic components.

[0186] In summary, this application provides an ionic conductivity acquisition device. For each inorganic component among the various inorganic components contained in the SEI membrane, the device can simulate the crystal structure and target structure of that inorganic component. The target structure can include at least one of amorphous structure, grain boundary structure, and pore interface structure. The pore interface structure is obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte. For each structure, both crystal and target, a computer device can obtain a second curve showing the force exerted on atoms in the structure over time, as well as the energy of the structure, based on the atomic trajectories and a first change curve of that structure. The ionic conductivity of the structure is then obtained based on the second change curve and the energy. Since various structures can be simulated and their ionic conductivity calculated by computer, the efficiency of acquiring the ionic conductivity of different types of structures in the SIE membrane is improved compared to related technologies. Furthermore, since not only the ionic conductivity of the crystal structure but also that of the target structure can be acquired, the ionic conductivity of the SEI membrane in multiple dimensions can be obtained, ensuring a more comprehensive acquisition of the ionic conductivity.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method of acquiring ion conductivity, characterized by, The method comprises: For each of a plurality of inorganic components included in the solid electrolyte interface film, simulating a crystal structure of the inorganic component and a target structure, the target structure comprising a pore interface structure, the pore interface structure being a structure obtained by taking a union of a first set of coordinates of atoms in a pore structure and a second set of coordinates of atoms in an electrolyte, the pore structure being obtained based on the crystal structure; For each of the crystal structure and the target structure, obtaining an atomic trajectory of the structure and a first variation curve of velocity over time, obtaining a second variation curve of force over time of atoms in the structure and an energy of the structure based on the atomic trajectory and the first variation curve, and obtaining an ionic conductivity of the structure based on the second variation curve and the energy.

2. The method of claim 1, wherein, The pore interface structure is a plurality, and the pore parameters of the pore structures corresponding to any two of the plurality of pore interface structures are different, the pore parameters comprising at least one of pore size and pore shape.

3. The method of claim 1, wherein, After obtaining the ionic conductivity of the structure, the method further comprises: obtaining a structure with the highest ionic conductivity from at least two of the ionic conductivities; adjusting manufacturing parameters of a lithium battery based on the structure with the highest ionic conductivity to increase the proportion of the structure with the highest ionic conductivity in the solid electrolyte interface film.

4. The method according to any one of claims 1 to 3, characterized in that, The target structure further comprises at least one of an amorphous structure and a grain boundary structure, obtaining a second variation curve of force over time of atoms in the structure and an energy of the structure based on the atomic trajectory and the first variation curve comprises: inputting the atomic trajectory and the first variation curve into a potential function model to obtain a second variation curve of force over time of atoms in the structure and an energy of the structure output by the potential function model.

5. The method of claim 4, wherein, The method further comprises: obtaining a first data set, the first data set comprising a first training parameter and a first label parameter of each of a plurality of sample crystal structures; obtaining a second data set, the second data set comprising a second training parameter and a second label parameter of each of a plurality of sample target structures, the sample target structure comprising a sample pore interface structure, the sample pore interface structure being a structure obtained by taking a union of a first sample set of coordinates of sample atoms in a sample pore structure and a second sample set of coordinates of sample atoms in a sample electrolyte; training the first data set and the second data set to obtain the potential function model; wherein each of the first training parameter and the second training parameter comprises a sample atomic trajectory and a sample first variation curve of sample velocity over time, and each of the first label parameter and the second label parameter comprises a sample second variation curve of sample force over time and a sample energy.

6. The method of claim 5, wherein, The sample target structure further comprises at least one of a sample amorphous structure and a sample grain boundary structure; before obtaining the first data set, the method further comprises: obtaining a plurality of sample inorganic components; Obtain the sample crystal structure of each of the inorganic components of the sample; When the target structure of the sample includes the amorphous structure of the sample, obtain the amorphous structures of various inorganic components of the sample; When the target sample structure includes the sample grain boundary structure, a predetermined number of first sample inorganic components are obtained from a variety of sample inorganic components, sample homophase grain boundaries of each first sample inorganic component are obtained, and sample heterophase grain boundaries composed of every two different first sample inorganic components are obtained, so as to obtain a variety of sample grain boundary structures. Based on at least one of the sample crystal structures, multiple sample pore structures are obtained, and the first set of sample coordinates of sample atoms in each sample pore structure is combined with the second set of sample coordinates of sample atoms in the sample electrolyte to obtain multiple sample pore interface structures. Each of the sample crystal structures corresponds to at least two sample pore structures, and the pore parameters of any two of the at least two sample pore structures are different. The pore parameters include at least one of sample shape and sample size.

7. The method of claim 6, wherein, The potential function model is obtained by training on the first dataset and the second dataset, including: The initial potential function model is trained using the first dataset to obtain the first potential function model; The first potential function model is trained using the second dataset to obtain the potential function model.

8. The method of claim 7, wherein, When the target structure of the sample includes the sample amorphous structure, the sample grain boundary structure, and the sample pore interface structure, the second dataset includes a first sub-dataset, a second sub-dataset, and a third sub-dataset. The first sub-dataset includes training parameters and label parameters for multiple sample amorphous structures, the second sub-dataset includes training parameters and label parameters for multiple sample grain boundary structures, and the third sub-dataset includes training parameters and label parameters for multiple sample pore interface structures. The first potential function model is trained using the second dataset to obtain the potential function model, including: The first potential function model is trained using the first subset of the dataset to obtain the second potential function model; The second potential function model is trained using the second subset of the dataset to obtain the third potential function model; The third potential function model is trained using the third subset of data to obtain the potential function model.

9. The method according to any one of claims 6 to 8, characterized in that, The grain boundary structure includes in-phase grain boundaries of the inorganic components, and out-of-phase grain boundaries formed by the inorganic components and other inorganic components.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the method for obtaining ionic conductivity as described in any one of claims 1-9.

11. A computer device, comprising: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method for obtaining ionic conductivity according to any one of claims 1-9.

12. An ion conductivity acquisition device, characterized by, include: The first acquisition module is used to simulate the crystal structure and target structure of each inorganic component among the various inorganic components contained in the solid electrolyte interface membrane. The target structure includes a pore interface structure, which is obtained by taking the union of the first set of coordinates of atoms in the pore structure and the second set of coordinates of atoms in the electrolyte. The pore structure is obtained based on the crystal structure. The second acquisition module is used to acquire, for each of the crystal structure and the target structure, the atomic trajectory of the structure and a first curve of velocity change over time, acquire, based on the atomic trajectory and the first curve of velocity change over time, a second curve of force on the atoms in the structure and the energy of the structure, and acquire, based on the second curve of velocity change and the energy, the ionic conductivity of the structure.

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