Halide solid electrolyte material recommendation method and device, equipment and storage medium

Abstract

The invention belongs to the technical field of computational material science, and discloses a halide solid electrolyte material recommendation method and device, equipment and a storage medium. The method comprises the following steps: acquiring a halide crystal material data set; screening the halide crystal material data set according to crystal structure information of each crystal sample in the halide crystal material data set to obtain a candidate material data set; and determining the halide solid electrolyte material to be recommended according to the energy characteristic parameter of each crystal sample in the candidate material data set. Due to the fact that the crystal structure calculation process is simple and high in efficiency, the crystal meeting adjustment can be rapidly obtained, the order of magnitude of the crystal material can be rapidly reduced, then the accurate halide solid electrolyte material conforming to the electrolyte material characteristics is found by calculating the energy characteristic parameters, and the recommendation efficiency of the halide solid electrolyte material is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computational materials science, and in particular to a halide solid electrolyte material recommendation method, device, equipment and storage medium. BACKGROUND

[0002] The discovery of new materials is of great significance to technological progress and social development. They not only drive the innovation of existing technologies, but also open up new application fields, providing new possibilities for solving global challenges such as energy, environment, and health.

[0003] However, most of the existing halide solid electrolyte design schemes are through continuous testing and optimization of known material systems, further using supercomputers for large-scale data simulation and analysis to predict the properties and behavior of materials, accelerating the design process of new materials to discover and create new material combinations, structures and properties, mainly in the iteration of system and performance. However, this way, the synthesis and characterization cost is higher in the iteration process, and the generation efficiency is too low.

[0004] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0005] The main purpose of the present application is to provide a halide solid electrolyte material recommendation method, device, equipment and storage medium, which aims to solve the technical problem of low efficiency in the process of discovering new materials in the prior art.

[0006] To achieve the above purpose, the present application provides a halide solid electrolyte material recommendation method, which comprises the following steps:

[0007] Obtain a halide crystal material dataset; filter the halide crystal material dataset according to the crystal structure information of each crystal sample in the halide crystal material dataset to obtain a candidate material dataset; determine the halide solid electrolyte material to be recommended according to the energy characteristic parameters of each crystal sample in the candidate material dataset.

[0008] It can be understood that in the process of filtering the halide crystal material dataset through the crystal structure information of each crystal sample, the crystal structure calculation process is simple and efficient, which can quickly obtain the adjusted crystal, quickly reduce the order of magnitude of the crystal material, and then find the more accurate halide solid electrolyte material that meets the characteristics of the electrolyte material by calculating the energy characteristic parameters, thereby improving the recommendation efficiency of the halide solid electrolyte material.

[0009] In some embodiments, the screening of the halide crystal material dataset according to the crystal structure information of each crystal sample in the halide crystal material dataset comprises at least one of the following: screening of the halide crystal material dataset according to the density of each crystal sample in the halide crystal material dataset; screening of the halide crystal material dataset according to the band gap of each crystal sample in the halide crystal material dataset; screening of the halide crystal material dataset according to the thermodynamic stability parameter of each crystal sample in the halide crystal material dataset.

[0010] It should be noted that the screening of the halide crystal material dataset according to the crystal structure information can be performed by various parameters. First, the halide solid-state electrolyte needs to have a low density because the crystal density is inversely proportional to the energy density. By density, materials with high energy density can be screened. Secondly, the band gap is used to screen electronic insulators because the solid-state electrolyte cannot conduct electricity, otherwise a short circuit will occur, which ensures the safety of the material. Finally, by judging the thermodynamic stability of the candidate material, the feasibility of experimental synthesis is improved. The higher the thermodynamic stability, the easier it is to synthesize.

[0011] In some embodiments, the screening of the halide crystal material dataset according to the thermodynamic stability parameter of each crystal sample in the halide crystal material dataset comprises: inputting a corresponding phase diagram model according to the crystal structure information of each crystal sample in the halide crystal material dataset to determine a corresponding crystal material convex hull energy; and screening the halide crystal material dataset according to the crystal material convex hull energy.

[0012] It should be noted that by constructing a convex hull, it can be directly judged whether a compound is thermodynamically stable. The convex hull energy is selected for thermodynamic stability judgment because it is intuitive and has high calculation efficiency, which improves the efficiency of the overall screening process and reflects the difficulty of material synthesis.

[0013] In some embodiments, the determination of the halide solid-state electrolyte material to be recommended according to the energy characteristic parameter of each crystal sample in the candidate material dataset comprises: determining a molecular structure parameter of each crystal in the candidate material dataset; determining a corresponding migration energy barrier according to the molecular structure parameter; and determining the halide solid-state electrolyte material to be recommended according to the corresponding migration energy barrier of the crystal material in the candidate material dataset.

[0014] It should be noted that by the method of calculating the migration energy barrier based on the molecular structure parameters, halide solid electrolyte materials with low migration energy barrier can be efficiently and accurately screened. This is because the ionic conductivity of solid electrolyte is crucial for the performance of electrochemical devices such as solid-state batteries. Higher ionic conductivity can improve the output power density and efficiency of electrochemical devices, and also can shorten the response time and charging and discharging time of electrochemical devices, etc.

[0015] In some embodiments, before determining the halide solid electrolyte material to be recommended according to the migration energy barrier of the crystal material corresponding to the candidate material data set, it further comprises: determining the molecular structure parameters of each crystal in the candidate material data set; determining the corresponding redox potential according to the molecular structure parameters; and determining the halide solid electrolyte material to be recommended according to the migration energy barrier and the redox potential of the crystal material corresponding to the candidate material data set.

[0016] It can be understood that the electrochemical window is used to judge the electrochemical stability. The electrochemical window directly gives the maximum voltage range that the material can withstand in the electrochemical environment. This allows researchers to clearly know the working voltage range of the material in practical application, i.e. the redox potential. By screening the electrochemical window to limit the range of electrode potential, the decomposition of electrolyte or the occurrence of irreversible processes such as redox reaction of electrode can be avoided, and the safety of the recommended material can be improved.

[0017] In some embodiments, before determining the halide solid electrolyte material to be recommended according to the migration energy barrier and the redox potential of the crystal material corresponding to the candidate material data set, it further comprises: determining the bulk modulus and shear modulus corresponding to the crystal structure information in the halide solid electrolyte material to be recommended; and determining the halide solid electrolyte material to be recommended according to the migration energy barrier, the redox potential, the bulk modulus and the shear modulus of the crystal material corresponding to the candidate material data set.

[0018] It should be noted that in order to further reduce the processing difficulty of the material and screen out materials with too high processing cost, it is proposed to screen by material processing characteristics, and the bulk modulus and shear modulus are used to determine the halide solid electrolyte material to be recommended, which can improve the processability of the recommended material.

[0019] In some embodiments, before the acquiring of the halide crystal material dataset, the method comprises: acquiring an inorganic crystal material dataset containing a plurality of inorganic crystal material samples; determining a structure total energy according to crystal structure information in the inorganic crystal material samples; and determining the halide crystal material dataset by matching the structure total energy with a preset halide crystal material energy interval.

[0020] It should be noted that the matching of the structure total energy with the preset halide crystal material energy interval can adapt the scheme to any large inorganic material database, and the energy screening can screen the inorganic materials to the halide crystal materials required by the scheme, the screening speed is fast, the efficiency is high, and the adaptability of the screening process is further improved.

[0021] In a second aspect, to achieve the above object, the present application further provides a halide solid-state electrolyte material recommendation device, characterized in that the halide solid-state electrolyte material recommendation device comprises: an acquisition module for acquiring a halide crystal material dataset; a processing module for screening the halide crystal material dataset according to crystal structure information of each crystal sample in the halide crystal material dataset to obtain a candidate material dataset; and the processing module is configured to determine a halide solid-state electrolyte material to be recommended according to energy characteristic parameters of each crystal sample in the candidate material dataset.

[0022] In a third aspect, to achieve the above object, the present application further provides a halide solid-state electrolyte material recommendation device, characterized in that the device comprises: a memory, a processor, and a halide solid-state electrolyte material recommendation program stored in the memory and executable on the processor, the halide solid-state electrolyte material recommendation program being configured to implement the steps of the halide solid-state electrolyte material recommendation method.

[0023] In a fourth aspect, a storage medium is provided, characterized in that the storage medium stores a halide solid-state electrolyte material recommendation program, the halide solid-state electrolyte material recommendation program being executable by a processor to implement the steps of the halide solid-state electrolyte material recommendation method. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 A flowchart of a first embodiment of the halide solid-state electrolyte material recommendation method of the present application;

[0025] Figure 2 A flowchart of a first embodiment of the halide solid-state electrolyte material recommendation method of the present application;

[0026] Figure 3 A flowchart of a first embodiment of the halide solid-state electrolyte material recommendation method of the present application;

[0027] Figure 4 Flowchart of the recommended method for the halide solid-state electrolyte material of the present application, an embodiment thereof;

[0028] Figure 5 Flowchart of the recommended method for the halide solid-state electrolyte material of the present application, an embodiment thereof;

[0029] Figure 6 Flowchart of the recommended method for the halide solid-state electrolyte material of the present application, an embodiment thereof;

[0030] Figure 7 Flowchart of the recommended method for the halide solid-state electrolyte material of the present application, an embodiment thereof;

[0031] Figure 8 Structure block diagram of the recommended device for the halide solid-state electrolyte material of the present application, a first embodiment thereof;

[0032] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments in conjunction with the accompanying drawings. DETAILED DESCRIPTION

[0033] The embodiments of the technical solutions of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and therefore only serve as examples, and cannot limit the protection scope of the present application.

[0034] 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 belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application; the terms "comprising" and "having," and any variations thereof, as used herein are intended to cover a non-exclusive inclusion.

[0035] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0036] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily independent or alternative embodiments to each other. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0037] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0038] In the description of the embodiments of the present application, the term "a plurality of" refers to two or more (including two), and similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0039] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0040] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connecting", "connecting", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0041] The content disclosed by the embodiments of the present application mainly applies to the recommendation process of solid electrolyte materials. Through a series of calculations or artificial intelligence methods, suitable materials for halide solid electrolytes are analyzed from a large number of position materials to accelerate the research and development progress of the materials. Since the space of the generated candidate materials is extremely large, it may take decades to complete using traditional theoretical calculation methods. Therefore, the scheme evaluates and tests mainstream general potential energy models and internalizes a series of models for analysis, such as CHGNet and MEGNet, which have excellent performance and relatively low calculation cost, to accelerate the screening of battery materials. Taking the CHGNet machine learning model as an example, the crystal structure of the candidate material is input, and the crystal structure and energy after rough optimization are output. The model exhibits high precision and low cost in the halide material system compared with other machine learning models. For example, the MEGNet machine learning model inputs the crystal structure after rough optimization and outputs the band gap and mechanical properties. With different calculation methods and models, the number of recommended materials is reduced by orders of magnitude, and solid electrolyte materials with excellent performance are screened for testing and detection, thereby improving the efficiency of material selection.

[0042] The halide solid electrolyte is a kind of solid material with halogen as the main component, which is used as the electrolyte of lithium ion battery or other types of battery. Compared with the traditional liquid electrolyte, the halide solid electrolyte has higher safety, better mechanical properties and wider working temperature range. Therefore, the following recommendation scheme is proposed for the characteristics of the halide solid electrolyte.

[0043] According to some embodiments of the present application, a halide solid electrolyte material recommendation method is provided as shown in Figure 1 The method comprises the following steps:

[0044] A halide crystal material dataset is obtained. The halide crystal material dataset is screened according to the crystal structure information of each crystal sample in the halide crystal material dataset to obtain a candidate material dataset. The halide solid electrolyte material to be recommended is determined according to the energy characteristic parameters of each crystal sample in the candidate material dataset.

[0045] It should be noted that the halide crystal material dataset can be an open source dataset or a dataset obtained by analyzing and sorting existing material data. The present embodiment does not limit this. The halide crystal material dataset stores a large amount of halide crystal material data. Each halide crystal material data can include crystal structure information, components and other information of the halide crystal material. The crystal structure information is the molecular structure of the material, which refers to the spatial arrangement of atoms in the molecule, including bond length, bond angle and dihedral angle, or the spatial coordinates of atoms in the molecule, molecular model, etc.

[0046] It can be understood that the screening according to the crystal structure information is performed first instead of directly screening according to the energy characteristics because the screening according to the energy characteristics is often very complex, and the calculation amount of each piece of data is large, so directly calculating parameters such as ionic conductivity and electrochemical properties from the initially large amount of data will greatly limit the size of the halide crystal material data set, and therefore, before the process of selecting the energy characteristic parameters, the crystal structure information needs to be screened first, and one or more rounds of screening are performed according to some physical characteristics (density, band gap, etc.) of the material to reduce the order of magnitude of the material, and then the process of calculating the energy characteristic parameters is performed to ensure the overall efficiency of the material recommendation process.

[0047] Further, since the solid-state electrolyte material is screened, the most important thing is to screen the energy characteristics of the material, and the energy characteristic parameters may be, for example, ionic conductivity, electrochemical window, etc., which are necessary characteristics for the material to meet the requirements of the solid-state electrolyte and are the focus of the screening process. However, because the analysis process is complex and often involves a large amount of simulation and calculation, the screening efficiency is extremely low if only the energy characteristic parameters are used, which is not suitable for industrial production requirements.

[0048] It can be understood that in the process of screening the halide crystal material data set according to the crystal structure information of each crystal sample in the halide crystal material data set, because the crystal structure calculation process is simple and efficient, the crystal that meets the adjustment can be quickly obtained, and the order of magnitude of the crystal material in the process is quickly reduced, and then the energy characteristic parameters are calculated to find the relatively accurate halide solid-state electrolyte material that meets the characteristics of the electrolyte material, thereby improving the efficiency of recommending the halide solid-state electrolyte material.

[0049] In some embodiments, as shown in Figure 7 The screening of the halide crystal material data set according to the crystal structure information of each crystal sample in the halide crystal material data set includes at least one of the following: screening the halide crystal material data set according to the density of each crystal sample in the halide crystal material data set; screening the halide crystal material data set according to the band gap of each crystal sample in the halide crystal material data set; and screening the halide crystal material data set according to the thermodynamic stability parameter of each crystal sample in the halide crystal material data set.

[0050] It can be understood that the screening of the halide crystal material data set according to the density of each crystal sample in the halide crystal material data set is because the halide solid-state electrolyte needs to have a low density, because the crystal density is inversely proportional to the energy density, and a material with a lower density can be selected, for example, a material with a density of p < 2.5 g / cm3 is selected as the data basis for the subsequent screening process.

[0051] Secondly, the halide crystal material dataset is screened according to the band gaps of the crystal samples in the halide crystal material dataset; because the main function of the solid-state electrolyte is to transport ions (such as lithium ions) in the battery while preventing the direct conduction of electrons to prevent short circuits. Therefore, the solid-state electrolyte needs to have high ionic conductivity and low electronic conductivity. The band gap is used to screen the electronic insulator, because the solid-state electrolyte cannot conduct electricity, otherwise a short circuit will occur. The specific screening process can be to select materials with a band gap Eg> 3eV as the data basis for the subsequent screening process.

[0052] Specifically, the halide crystal material dataset is screened according to the thermodynamic stability parameters of the crystal samples in the halide crystal material dataset. The thermodynamic stability of the candidate material can be determined by a phase diagram to improve the feasibility of experimental synthesis. It should be noted that in order to ensure efficiency, the first thermodynamic stability calculation process can use a machine learning model for identification, for example: a CHGNet machine learning model, which outputs thermodynamic parameters such as convex hull energy to determine the thermodynamic stability, such as materials with a convex hull energy Ehull< 50meV / atom being considered to have higher thermal stability, and the selected materials being used as the data basis for the subsequent screening process. The current thermodynamic stability parameter is a parameter related parameter that can be used to represent the thermodynamic stability. In addition to the convex hull energy, it can also be the formation energy or the Gibbs free energy, and different representation methods have different accuracy and calculation efficiency. The present embodiment does not limit this, and the convex hull energy is preferably used based on the accuracy and calculation efficiency.

[0053] In a specific implementation, the above-mentioned three screenings of the halide crystal material dataset according to the crystal structure information of the crystal samples in the halide crystal material dataset can be used simultaneously, or one or two of them can be used according to business needs, for example: in order to find a material with higher synthesis feasibility and higher energy density, the halide crystal material dataset can be screened according to the density of the crystal samples in the halide crystal material dataset to obtain a first halide crystal material dataset, then the halide crystal material dataset is screened according to the thermodynamic stability parameters of the crystal samples in the first halide crystal material dataset to obtain a second halide crystal material dataset, and finally the material data in the second halide crystal material dataset is screened in the energy characteristic parameter direction to select the final material that meets the conditions of the solid-state electrolyte to obtain a solid-state electrolyte material with higher energy density and higher synthesis feasibility.

[0054] The order of the three screening methods is not limited in the embodiment, and the embodiment proposes a preferred scheme for screening the halide crystal material dataset according to the crystal structure information of each crystal sample in the halide crystal material dataset, for example: screening the halide crystal material dataset according to the density of each crystal sample in the halide crystal material dataset to obtain a first halide crystal material dataset; screening the halide crystal material dataset according to the band gap of each crystal sample in the first halide crystal material dataset to obtain a second halide crystal material dataset; screening the halide crystal material dataset according to the thermodynamic stability parameter of each crystal sample in the second halide crystal material dataset to obtain a third halide crystal material dataset, and determining the halide solid-state electrolyte material to be recommended according to the energy characteristic parameter of each crystal sample in the third halide crystal material dataset.

[0055] It should be noted that the screening of the halide crystal material dataset by the crystal structure information can be performed by multiple parameters. First, the halide solid-state electrolyte needs to have a low density, because the crystal density is inversely proportional to the energy density, and the materials with high energy density can be screened by the density. Secondly, the band gap is used to screen the electronic insulator, because the solid-state electrolyte cannot conduct electricity, otherwise a short circuit will occur, which ensures the safety of the material. Finally, the thermodynamic stability of the candidate material is determined to improve the feasibility of experimental synthesis. The higher the thermodynamic stability, the easier the synthesis.

[0056] In some embodiments, the screening of the halide crystal material dataset according to the thermodynamic stability parameter of each crystal sample in the halide crystal material dataset includes: inputting a corresponding phase diagram model according to the crystal structure information of each crystal sample in the halide crystal material dataset to determine a corresponding crystal material convex hull energy; and screening the halide crystal material dataset according to the crystal material convex hull energy.

[0057] It can be understood that, since this screening process is in the front position of the screening process, efficiency is required, and therefore the generation process of the phase diagram model can be automatically generated by a machine model to further improve the efficiency of obtaining the convex hull energy.

[0058] Specifically, the determination process of the convex hull energy can be, for example: inputting the chemical composition of the crystal material into the phase diagram model to obtain related parameters of the formation energy, and then calculating Ehull = Ecompound - Estable phase; wherein, Ehull is the convex hull energy; Ecompound is the formation energy of the target compound; Estable phase is the formation energy of the stable phase of the same composition on the convex hull boundary, and the screening standard can be defined as the material with Ehull < 50 meV / atom meets the condition of entering the next layer of screening.

[0059] It should be noted that by constructing the convex hull, it can be intuitively judged whether a compound is thermodynamically stable. The convex hull can be used to judge the thermodynamic stability because it is intuitive and has high calculation efficiency, which improves the efficiency of the overall screening process and also reflects the difficulty of material synthesis.

[0060] In some embodiments, as shown in Figure 3 determining the halide solid-state electrolyte material to be recommended according to the energy characteristic parameters of each crystal sample in the candidate material data set, includes: determining the molecular structure parameters of each crystal in the candidate material data set; determining the corresponding migration energy barrier according to the molecular structure parameters; determining the halide solid-state electrolyte material to be recommended according to the corresponding migration energy barrier of the crystal material in the candidate material data set.

[0061] It should be noted that since the purpose of the final screening is the solid-state electrolyte material, the most important thing is to obtain a material with high ionic conductivity. The ionic conductivity of the solid-state electrolyte is crucial to the performance of electrochemical devices such as solid-state batteries. Higher ionic conductivity can improve the output power density and efficiency of electrochemical devices, and also can shorten the response time and charging and discharging time of electrochemical devices, etc.

[0062] It can be understood that the ionic conductivity can be characterized as the migration energy barrier, which refers to the energy barrier that needs to be overcome in the process of atoms or ions migrating from one equilibrium position to another equilibrium position in a solid material. The migration energy barrier directly reflects the diffusion mechanism of ions in the material. By calculating and analyzing the migration energy barrier, it can be understood how ions move in the material, including the migration path, the height and position of the energy barrier, etc. Specifically, in the material screening stage, by calculating the migration energy barrier, materials with high migration energy barrier and low ionic conductivity can be quickly excluded, and resources can be concentrated on researching more potential materials, which has higher screening efficiency than other characterization parameters. The migration energy barrier can be calculated by first-principles calculation (such as density functional theory, DFT), molecular dynamics simulation (MD) or transition state theory (TST), which is not limited in the present embodiment.

[0063] It should be noted that by calculating the migration energy barrier based on the molecular structure parameters, halide solid-state electrolyte materials with low migration energy barrier can be efficiently and accurately screened. This is because the ionic conductivity of the solid-state electrolyte is crucial to the performance of electrochemical devices such as solid-state batteries. Higher ionic conductivity can improve the output power density and efficiency of electrochemical devices, and also can shorten the response time and charging and discharging time of electrochemical devices, etc.

[0064] In some embodiments, as shown in Figure 4Before determining the halide solid-state electrolyte material to be recommended according to the migration energy barrier corresponding to the crystal material in the candidate material data set, the method further comprises: determining the molecular structure parameters of each crystal in the candidate material data set; determining the corresponding redox potential according to the molecular structure parameters; and determining the halide solid-state electrolyte material to be recommended according to the migration energy barrier corresponding to the crystal material in the candidate material data set and the redox potential.

[0065] It should be noted that according to the molecular structure parameters of each crystal in the candidate material data set, the Gibbs free energy change of the redox reaction is determined by calculating the total energy of the material in different redox states, and the redox potential of the material is calculated according to the Gibbs free energy change and the potential of the standard hydrogen electrode (SHE). The electrochemical window of the solid-state electrolyte refers to the range of electrode potential in the solid-state electrolyte system by adding an electrochemical window (Electrochemical Window) in the electrochemical test to avoid the occurrence of irreversible processes such as decomposition of electrolyte or redox reaction of electrode, and the upper limit of the electrochemical window is usually determined by the stability of the electrolyte at high potential. When the potential exceeds this upper limit, the electrolyte may be oxidized and decomposed to generate gas or other by-products, resulting in failure of the electrolyte. The lower limit of the electrochemical window is usually determined by the stability of the electrolyte at low potential. When the potential is lower than this lower limit, the electrolyte may be reduced and decomposed to generate metal deposition or other by-products, also resulting in failure of the electrolyte. The specific screening process may be, for example: selecting Ered<1V, Eox>3V, wherein the reduction potential (Reduction Potential, Ered) and the oxidation potential (Oxidation Potential, Eox).

[0066] It can be understood that the electrochemical window directly gives the maximum voltage range that the material can withstand in the electrochemical environment, which enables researchers to clearly know the working voltage range of the material in actual application, i.e., the redox potential. By determining the electrochemical window to limit the range of electrode potential to avoid the occurrence of irreversible processes such as decomposition of electrolyte or redox reaction of electrode, the safety of the recommended material is improved.

[0067] In some embodiments, as Figure 5The method shown in FIG5 further includes: determining the halide solid electrolyte material to be recommended based on the migration energy barrier and redox potential corresponding to the crystal material in the candidate material data set, and determining the corresponding bulk modulus and shear modulus based on the crystal structure information in the halide solid electrolyte material to be recommended; the method shown in FIG5 further includes: determining the halide solid electrolyte material to be recommended based on the migration energy barrier and redox potential corresponding to the crystal material in the candidate material data set, and determining the halide solid electrolyte material to be recommended based on the migration energy barrier, redox potential, bulk modulus and shear modulus corresponding to the crystal material in the candidate material data set.

[0068] It can be explained that, on the basis of electrochemical stability and ionic conductivity screening, the difficulty of material processing can also be considered. In this embodiment, bulk modulus and shear modulus are added for screening. Specifically, the calculation method of bulk modulus and shear modulus can be through a machine model, for example: MEGNet machine learning model, by inputting the roughly optimized crystal structure, outputting mechanical properties such as bulk modulus and shear modulus. The specific screening process can be, for example: K bulk modulus and G shear modulus, both of which are less than the modulus threshold of 30GPa. The preferred modulus threshold can be set at 20-40GPa, which is more reasonable. The smaller the better the processing, the specific value can be set according to the final required material order of magnitude.

[0069] It should be noted that in order to further reduce the difficulty of material processing and screen out materials with too high processing costs, it is proposed to screen by material processing characteristics, and determine the recommended halide solid electrolyte material based on the bulk modulus and shear modulus, which can improve the machinability of the recommended material.

[0070] In some embodiments, as Figure 6 Before obtaining the halide crystal material data set, the method includes: obtaining an inorganic crystal material data set, which contains multiple inorganic crystal material samples; determining the total structure energy based on the crystal structure information in the inorganic crystal material samples; and matching the total structure energy with a preset halide crystal material energy range to determine the halide crystal material data set.

[0071] It should be noted that in order to adapt this solution to any large-scale inorganic material database, this embodiment adaptively proposes a process for screening halide crystal materials, eliminating non-halides. Specifically, the calculated total structural energy is compared with the preset energy range of halide crystal materials to determine which materials meet the requirements. For example, the electronic structure and total energy of the material are calculated based on density functional theory (DFT) using software such as Gaussian. To improve efficiency, it is only necessary to compare whether the total energy is within the total energy range of the halide crystal material.

[0072] It should be noted that by matching the total energy of the structure with the preset halide crystal material energy interval, the scheme can adapt to any large inorganic material database. By energy screening, inorganic materials can be screened to the halide crystal materials required by the scheme. Screening by energy interval is fast and efficient, further improving the adaptability of the screening process.

[0073] In some embodiments, as shown in Figure 7 The preferred recommendation scheme is proposed, for example, screening based on lithium / sodium-containing halides as data (better screening results can be obtained), as shown in Figure 7 The ML refers to the calculation of parameters by machine learning (Machine Learning), and the DFT refers to the density functional theory (Density Functional Theory, DFT), which is a quantum mechanical method widely used to calculate the electronic structure and thermodynamic properties of materials. Through DFT calculation, the thermodynamic stability of the material can be evaluated, which is more accurate than the machine learning method, but the calculation efficiency is relatively low, so the DFT calculation needs to be placed in the order of magnitude of the number of materials to be screened. The ion conductance BV refers to the calculation of BV (Berthelot-Volmer) theory or model. The specific scheme can be: screening the first round according to the density; screening the second round according to the band gap; completing the third round of screening according to the calculation of the thermodynamic stability of the model; screening the fourth round according to the electrochemical stability; completing the fifth round of screening according to the calculation of the thermodynamic stability of the DFT theory; screening the sixth round according to the ion conductance; screening the seventh round according to the elastic modulus, and obtaining the final recommended halide crystal material.

[0074] The second aspect is to achieve the above-mentioned purpose, as shown in Figure 8 The application also provides a halide solid electrolyte material recommendation device, characterized in that the halide solid electrolyte material recommendation device comprises: an acquisition module for acquiring a halide crystal material dataset; a processing module for screening the halide crystal material dataset according to the crystal structure information of each crystal sample in the halide crystal material dataset to obtain a candidate material dataset; and the processing module is configured to determine the halide solid electrolyte material to be recommended according to the energy characteristic parameters of each crystal sample in the candidate material dataset.

[0075] The third aspect is to achieve the above-mentioned purpose, and the application further provides a halide solid electrolyte material recommendation device, characterized in that the device comprises: a memory, a processor, and a halide solid electrolyte material recommendation program stored on the memory and executable on the processor, wherein the halide solid electrolyte material recommendation program is configured to implement the steps of the halide solid electrolyte material recommendation method.

[0076] In a fourth aspect, a storage medium, characterized in that a halide solid-state electrolyte material recommendation program is stored on the storage medium, and the halide solid-state electrolyte material recommendation program, when executed by a processor, implements the steps of the halide solid-state electrolyte material recommendation method.

[0077] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the description of the present application. In particular, as long as there is no structural conflict, each technical feature mentioned in each embodiment can be combined in any way. The present 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 for recommending a halide solid electrolyte material, characterized in that: The recommended method for the halide solid electrolyte material includes: Get halide crystal material dataset; screening the halide crystal material dataset according to the crystal structure information of each crystal sample in the halide crystal material dataset to obtain a candidate material dataset; The halide solid electrolyte material to be recommended is determined according to the energy characteristic parameters of each crystal sample in the candidate material data set.

2. The method according to claim 1, wherein The screening of the halide crystal material dataset according to the crystal structure information of each crystal sample in the halide crystal material dataset comprises at least one of the following: screening the halide crystal material dataset according to the density of each crystal sample in the halide crystal material dataset; screening the halide crystal material dataset according to the band gap of each crystal sample in the halide crystal material dataset; The halide crystal material dataset is screened according to the thermodynamic stability parameter of each crystal sample in the halide crystal material dataset.

3. The method according to claim 2, wherein The screening of the halide crystal material dataset according to the thermodynamic stability parameter of each crystal sample in the halide crystal material dataset comprises: According to the crystal structure information of each crystal sample in the halide crystal material data set, the corresponding phase diagram model is input to determine the corresponding crystal material convex hull energy; The halide crystal material dataset can be screened according to the crystal material convex hull.

4. The method according to claim 1, wherein The determining of the halide solid electrolyte material to be recommended according to the energy characteristic parameters of each crystal sample in the candidate material data set includes: According to the molecular structure parameters of each crystal in the candidate material data set; Determining the corresponding migration energy barrier according to the molecular structure parameters; The halide solid electrolyte material to be recommended is determined according to the migration energy barrier corresponding to the crystal material in the candidate material data set.

5. The method according to claim 4, wherein Before determining the halide solid electrolyte material to be recommended based on the migration energy barrier corresponding to the crystal material in the candidate material data set, the method further includes: According to the molecular structure parameters of each crystal in the candidate material data set; Determining the corresponding redox potential according to the molecular structure parameters; The determining of the halide solid electrolyte material to be recommended based on the migration energy barrier corresponding to the crystal material in the candidate material data set further includes: The halide solid electrolyte material to be recommended is determined according to the migration energy barrier and redox potential corresponding to the crystal material in the candidate material data set.

6. The method according to claim 5, wherein Before determining the halide solid electrolyte material to be recommended based on the migration energy barrier and redox potential corresponding to the crystal material in the candidate material data set, the method further includes: Determining the corresponding bulk modulus and shear modulus according to the crystal structure information of the halide solid electrolyte material to be recommended; The step of determining the halide solid electrolyte material to be recommended based on the migration energy barrier and redox potential corresponding to the crystal material in the candidate material data set further includes: The halide solid electrolyte material to be recommended is determined according to the migration energy barrier, redox potential, bulk modulus and shear modulus corresponding to the crystal material in the candidate material data set.

7. The method according to claim 1, wherein Before obtaining the halide crystal material dataset, the method includes: Acquire an inorganic crystal material data set, wherein the inorganic crystal material data set includes a plurality of inorganic crystal material samples; determining the total structural energy based on the crystal structure information of the inorganic crystal material sample; A halide crystal material data set is determined by matching the total energy of the structure with a preset halide crystal material energy range.

8. A halide solid electrolyte material recommendation device, characterized in that: The halide solid electrolyte material recommended device includes: An acquisition module, used to acquire a halide crystal material dataset; a processing module, configured to screen the halide crystal material dataset according to the crystal structure information of each crystal sample in the halide crystal material dataset to obtain a candidate material dataset; The processing module is used to determine the halide solid electrolyte material to be recommended based on the energy characteristic parameters of each crystal sample in the candidate material data set.

9. A halide solid electrolyte material recommendation device, characterized in that: The device includes: a memory, a processor, and a halide solid electrolyte material recommendation program stored in the memory and executable on the processor, wherein the halide solid electrolyte material recommendation program is configured to implement the steps of the halide solid electrolyte material recommendation method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a halide solid electrolyte material recommendation program, which, when executed by a processor, implements the steps of the halide solid electrolyte material recommendation method according to any one of claims 1 to 7.

Images