Electrolyte performance prediction method and electrolyte formula determination method and device

Abstract

The invention provides an electrolyte performance prediction method and an electrolyte formula determination method and device, and relates to the technical field of electrolyte performance prediction. The electrolyte performance prediction method comprises the following steps: determining a formula proportion of an electrolyte to be tested; on the basis of a first principle calculation mode, respective microscopic chemical property information of a plurality of electrolyte molecules in the electrolyte to be detected is determined; inputting the respective microscopic chemical property information of the plurality of electrolyte molecules and the formula proportion into an electrolyte performance prediction model to obtain a performance prediction result of the to-be-tested electrolyte output by the electrolyte performance prediction model; wherein the electrolyte performance prediction model has learned to obtain the electrolyte performance prediction capability based on the formula proportion of the electrolyte and the micro chemical property information of electrolyte molecules. The electrolyte performance prediction efficiency can be improved, the experiment cost is reduced, and the performance prediction accuracy is ensured.

Description

Technical Field

[0001] This invention relates to the field of electrolyte performance prediction technology, specifically to a method for predicting electrolyte performance, a method for determining electrolyte formulation, and an apparatus. Background Technology

[0002] Lithium-ion battery electrolytes have a crucial impact on battery life and safety. While traditional experimental methods are used to study electrolyte performance, this process is not only time-consuming and costly. Therefore, there is an urgent need for a method to improve the efficiency of predicting lithium-ion battery electrolyte performance. Summary of the Invention

[0003] In view of this, the present invention aims to provide a method for predicting electrolyte performance, a method for determining electrolyte formulation, and an apparatus to solve the problems of long cycle and time-consuming and labor-intensive electrolyte performance prediction in the prior art.

[0004] A first aspect of the present invention provides a method for predicting electrolyte performance, comprising: determining the formulation ratio of an electrolyte to be tested; determining the microscopic chemical properties of multiple electrolyte molecules within the electrolyte to be tested based on first-principles calculations; inputting the microscopic chemical properties of the multiple electrolyte molecules and the formulation ratio into an electrolyte performance prediction model to obtain a performance prediction result of the electrolyte to be tested output by the electrolyte performance prediction model; wherein the electrolyte performance prediction model has learned the ability to predict electrolyte performance based on the formulation ratio and the microscopic chemical properties of electrolyte molecules.

[0005] In some embodiments, determining the microscopic chemical properties of multiple electrolyte molecules within the electrolyte to be tested using first-principles calculations includes: identifying a first electrolyte molecule and a second electrolyte molecule among the multiple electrolyte molecules; the microscopic chemical properties of the first electrolyte molecule are included in a molecular microscopic chemical property library; the microscopic chemical properties of the second electrolyte molecule are not included in the molecular microscopic chemical property library; the molecular microscopic chemical property library includes the calculated microscopic chemical properties of multiple electrolyte molecules, and the microscopic chemical properties of each calculated electrolyte molecule are obtained by first-principles calculation based on the molecular structure of the electrolyte molecule; determining the microscopic chemical properties of the first electrolyte molecule from the molecular microscopic chemical property library; and obtaining the microscopic chemical properties of the second electrolyte molecule by performing first-principles calculations based on the molecular structure of the second electrolyte molecule.

[0006] In some embodiments, the method further includes: supplementing the molecular microchemical property library with the microchemical property information of the second electrolyte molecules.

[0007] Furthermore, the electrolyte performance prediction model is trained in the following manner: determining the formulation ratio sample corresponding to the electrolyte sample; determining the microscopic chemical property information sample corresponding to the electrolyte sample based on the first principle calculation method; determining the performance label value of the electrolyte sample; and training the initial electrolyte performance prediction model based on the training sample determined by the formulation ratio sample, the microscopic chemical property information sample, and the performance label value to obtain the trained electrolyte performance prediction model.

[0008] As one possible implementation, determining the performance label value of the electrolyte sample includes: performing molecular dynamics simulations based on the formulation ratio sample to obtain simulation results; and determining the performance label value based on the simulation results.

[0009] A second aspect of the present invention provides a method for determining an electrolyte formulation, comprising: obtaining performance prediction results for electrolytes with different formulations based on the electrolyte performance prediction method described in the first aspect; and determining a target electrolyte formulation from the different formulations based on the performance prediction results.

[0010] In some embodiments, determining the target electrolyte formulation from the different formulations based on the performance prediction results includes: determining a first target electrolyte formulation from the different formulations based on the performance prediction results; wherein the first target electrolyte formulation is the formulation corresponding to an electrolyte whose performance prediction results meet preset conditions; verifying the performance prediction results of the first target electrolyte formulation based on the molecular dynamics simulation results of the first target electrolyte formulation; and if the verification is successful, determining the first target electrolyte formulation as the target electrolyte formulation.

[0011] A third aspect of the present invention provides an electrolyte performance prediction device, comprising: a first determining module for determining the formulation ratio of an electrolyte to be tested; a second determining module for determining the microscopic chemical properties of multiple electrolyte molecules within the electrolyte to be tested based on first-principles calculations; and a prediction module for inputting the microscopic chemical properties of the multiple electrolyte molecules and the formulation ratio into an electrolyte performance prediction model to obtain a performance prediction result of the electrolyte to be tested output by the electrolyte performance prediction model; wherein the electrolyte performance prediction model has learned the ability to predict the performance of an electrolyte formulation based on the electrolyte formulation ratio and the microscopic chemical properties of the electrolyte molecules.

[0012] A fourth aspect of the present invention provides an apparatus for determining an electrolyte formulation, comprising: an acquisition module for obtaining performance prediction results of electrolytes with different formulations based on the electrolyte performance prediction method described in the first aspect; and a determination module for determining a target electrolyte formulation from the plurality of formulations based on the performance prediction results.

[0013] The fifth aspect of the present invention provides an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the computer program to implement the electrolyte performance prediction method described in the first aspect above, and / or to implement the electrolyte formulation determination method described in the second aspect above.

[0014] According to the electrolyte performance prediction method, electrolyte formulation determination method, and apparatus provided by this invention, the formulation ratio of the electrolyte to be tested is determined; based on first-principles calculations, the microscopic chemical properties of multiple electrolyte molecules within the electrolyte to be tested are determined; the microscopic chemical properties of the multiple electrolyte molecules and the formulation ratio are input into an electrolyte performance prediction model to obtain the performance prediction result of the electrolyte to be tested output by the electrolyte performance prediction model; wherein, the electrolyte performance prediction model has learned the ability to predict electrolyte performance based on the electrolyte formulation ratio and the microscopic chemical properties of electrolyte molecules. This invention combines first-principles calculations and an electrolyte performance prediction model, enabling electrolyte performance prediction without traditional experimental procedures. This not only improves the efficiency of electrolyte performance prediction and reduces experimental costs but also ensures the accuracy of electrolyte performance prediction, thereby shortening the electrolyte development cycle and reducing development costs. Furthermore, the microscopic chemical property information obtained based on first-principles calculations can be used to explain electrolyte performance at the microscopic mechanism level, enhancing technical transparency and credibility. Attached Figure Description

[0015] Figure 1 This is one of the flowcharts illustrating an electrolyte performance prediction method provided in an embodiment of the present invention;

[0016] Figure 2 This is a second schematic flowchart of the electrolyte performance prediction method provided in an embodiment of the present invention;

[0017] Figure 3 The third schematic flowchart of the electrolyte performance prediction method provided in the embodiment of the present invention;

[0018] Figure 4 A flowchart illustrating the method for determining the electrolyte formulation provided in an embodiment of the present invention;

[0019] Figure 5 This is a schematic diagram of the electrolyte performance prediction device provided in an embodiment of the present invention;

[0020] Figure 6 A schematic diagram of the structure of the electrolyte formulation determination device provided in an embodiment of the present invention;

[0021] Figure 7 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It is important to note that lithium-ion battery electrolytes have a crucial impact on battery life and safety. Currently, the main methods for determining the performance of lithium-ion battery electrolytes fall into three categories: traditional experimental methods, machine learning prediction methods, and materials science computational methods. Traditional experimental methods are time-consuming and labor-intensive. While machine learning prediction methods can accelerate predictions, they require a large amount of high-quality data and cannot explain microscopic mechanisms. Materials science computational methods, such as first-principles calculations, can delve into microstructure and reaction mechanisms, but they are computationally intensive and inefficient, making them unsuitable for large-scale applications. Therefore, there is an urgent need for a method that can improve the efficiency of predicting the performance of lithium-ion battery electrolytes.

[0024] To address the aforementioned problems, this invention provides a method for predicting electrolyte performance, a method for determining electrolyte formulation, and an apparatus.

[0025] Figure 1 This is one of the flowcharts illustrating an electrolyte performance prediction method provided in an embodiment of the present invention. For example... Figure 1 As shown, the electrolyte performance prediction method of this invention may include the following steps:

[0026] Step 101: Determine the formulation ratio of the electrolyte to be tested.

[0027] The formulation ratio of the electrolyte to be tested refers to the concentration ratio between each electrolyte molecule in the electrolyte to be tested. Each electrolyte molecule in the electrolyte to be tested includes both solute molecules and solvent molecules.

[0028] Step 102: Based on first-principles calculations, determine the microscopic chemical properties of each electrolyte molecule in the electrolyte to be tested.

[0029] Among these, quantum mechanics-based computational methods are used to calculate and predict the electronic structure, energy, mechanical behavior, and other properties of matter, starting from fundamental physical laws. Microscopic chemical properties of electrolyte molecules can include electronic structure, molecular orbital energies, and more, such as: highest occupied molecular orbital (HOMO) energy level, lowest unoccupied molecular orbital (LUMO) energy level, HOMO-LUMO band gap, bond length (length of different chemical bonds), bond angle (size of bond angles between atoms within a molecule), dihedral angle (describing the torsional configuration of a molecule), total molecular energy (reflecting molecular stability; lower energy generally indicates greater stability), and bond dissociation energy (the dissociation energy of a specific chemical bond, used to assess the stability and reactivity of a molecule in chemical reactions).

[0030] In some embodiments, the microscopic chemical properties of each electrolyte molecule in the electrolyte to be tested are determined by first-principles calculations. Specifically, the preliminary work includes: (1) Molecular model construction: Based on the chemical composition and structural information of the electrolyte molecules, the three-dimensional structure is constructed using the molecular modeling software Gaussian View; (2) The calculation software is Gaussian, and the theoretical method is based on density functional theory (DFT), which transforms the problem of multi-electron systems into a single-electron problem. The wave function and energy of the electron are obtained by solving the Kohn-Sham equation; (3) Initial parameter settings: The exchange correlation functional is a hybrid functional (B3LYP), the basis sets are the commonly used basis sets 6-31G(d,p) and cc-pvdz, and the solvent effect is simulated by the implicit solvent model PCM to simulate the electrolyte environment.

[0031] Furthermore, based on first-principles calculations, the calculation process for determining the microscopic chemical properties of each electrolyte molecule in the electrolyte to be tested includes: (1) structural optimization, performing geometric optimization on the molecule to obtain the lowest energy structure and outputting geometric parameters such as bond length, bond angle, and dihedral angle; (2) electronic structure calculation, analyzing the HOMO / LUMO energy level difference (ΔE) to predict redox stability. The HOMO energy level determines the reduction stability of the molecule (compatibility with lithium metal), and the LUMO energy level determines the oxidation stability (compatibility with cathode materials); (3) spectral simulation, calculating infrared (IR) and Raman spectra, and comparing them with experimental data to verify the accuracy of the model.

[0032] In some embodiments, to improve the efficiency of electrolyte performance prediction, electrolyte molecules with good performance can be pre-screened from literature, experimental data, or existing electrolyte formulations to construct an electrolyte molecule database. Based on the first-principles calculations described above, the microscopic chemical properties of each electrolyte molecule in the database are determined. Based on the microscopic chemical properties of each electrolyte molecule in the database, a molecular microscopic chemical property library is constructed. When determining the individual microscopic chemical properties of multiple electrolyte molecules in the electrolyte to be tested, the microscopic chemical properties of each electrolyte molecule in the electrolyte to be tested can be directly queried from the molecular microscopic chemical property library, eliminating the need for first-principles calculations in each prediction process.

[0033] Step 103: Input the microscopic chemical properties and formulation ratios of multiple electrolyte molecules into the electrolyte performance prediction model to obtain the performance prediction results of the electrolyte to be tested output by the electrolyte performance prediction model; wherein, the electrolyte performance prediction model has learned the ability to predict electrolyte performance based on the formulation ratio and microscopic chemical properties of electrolyte molecules.

[0034] Since the microscopic chemical properties of electrolyte molecules, such as electronic structure and molecular orbital energy, can reveal the interaction between the solvent and lithium salt in the electrolyte, the solvation effect, and possible chemical reaction pathways, and have a direct or indirect relationship with the key performance characteristics of the electrolyte, such as stability, conductivity, and safety, this invention proposes to predict the performance of electrolytes by using the microscopic chemical properties of electrolyte molecules calculated based on the formulation ratio and first-principles calculations.

[0035] In some embodiments, the electrolyte performance prediction model can be a machine learning model, which combines first-principles calculations with machine learning models. This not only enables efficient prediction of electrolyte performance, but also allows the microscopic chemical property information obtained from first-principles calculations to serve as a basis for explaining microscopic mechanisms.

[0036] The performance of an electrolyte can include its ionic conductivity, dielectric constant, melting point, boiling point, and ion transport number under different environmental conditions. An electrolyte performance prediction model can predict one or more of the electrolyte's properties based on the formulation ratio and the microscopic chemical properties of the electrolyte molecules. That is, the model's output performance prediction results can include predicted values ​​for at least one of the following: ionic conductivity, dielectric constant, melting point, boiling point, and ion transport number, under different environmental conditions.

[0037] In some embodiments, the training samples for the electrolyte performance prediction model may include a formulation ratio sample determined based on an electrolyte sample, a sample of microscopic chemical properties of electrolyte molecules determined based on an electrolyte sample, and performance label values ​​of the electrolyte sample.

[0038] According to an embodiment of the present invention, the electrolyte performance prediction method involves determining the formulation ratio of the electrolyte to be tested; determining the microscopic chemical properties of multiple electrolyte molecules within the electrolyte based on first-principles calculations; inputting the microscopic chemical properties of the multiple electrolyte molecules and the formulation ratio into an electrolyte performance prediction model to obtain the performance prediction result of the electrolyte output by the electrolyte performance prediction model; wherein the electrolyte performance prediction model has learned the ability to predict electrolyte performance based on the electrolyte formulation ratio and the microscopic chemical properties of electrolyte molecules. This invention combines first-principles calculations and an electrolyte performance prediction model, enabling electrolyte performance prediction without traditional experimental procedures. This not only improves the efficiency of electrolyte performance prediction and reduces experimental costs but also ensures the accuracy of electrolyte performance prediction, thereby shortening the electrolyte development cycle and reducing development costs. Furthermore, the microscopic chemical property information obtained based on first-principles calculations can be used to explain electrolyte performance at the microscopic mechanism level, enhancing technical transparency and credibility.

[0039] To further improve the efficiency of electrolyte performance prediction, the present invention also provides another embodiment. Figure 2 This is a second schematic flowchart of the electrolyte performance prediction method provided in an embodiment of the present invention. Figure 2 As shown, based on the above embodiments, Figure 1 The implementation process of step 102 may include the following steps:

[0040] Step 201: Identify the first electrolyte molecule and the second electrolyte molecule among multiple electrolyte molecules; the microscopic chemical properties of the first electrolyte molecule are included in the molecular microscopic chemical property library; the microscopic chemical properties of the second electrolyte molecule are not included in the molecular microscopic chemical property library; the molecular microscopic chemical property library includes the microscopic chemical properties of multiple calculated electrolyte molecules, and the microscopic chemical properties of each calculated electrolyte molecule are obtained by first-principles calculations based on the molecular structure of the electrolyte molecule.

[0041] The molecular microchemical property library includes the calculated microchemical properties of multiple electrolyte molecules. As an example, the molecular microchemical property library can be pre-constructed. First, the microchemical properties of all electrolyte molecules with good performance can be calculated using first-principles calculations. Then, these individual microchemical properties are used to assemble the molecular microchemical property library, meaning the calculated electrolyte molecules represent all electrolyte molecules with good performance. Specifically, electrolyte molecules with good performance can be pre-selected from literature, experimental data, or existing electrolyte formulations. The selection criteria can be set based on actual needs. An electrolyte molecule database is constructed, and the microchemical properties of each electrolyte molecule in the database are determined using the first-principles calculations described above. Based on the microchemical properties of each electrolyte molecule in the database, the molecular microchemical property library is constructed.

[0042] As another example, the initial molecular microchemical property library is empty. As the electrolyte performance prediction method is executed, the latest calculated microchemical property information of each electrolyte molecule is stored in the molecular microchemical property library each time, based on first-principles calculations. That is, the molecular microchemical property library includes the microchemical property information of all electrolyte molecules that have been calculated in the electrolyte performance prediction process.

[0043] In some embodiments, multiple electrolyte molecules in the electrolyte to be tested can be queried in a molecular microscopic chemical property library. If the microscopic chemical property information of electrolyte molecule a in the electrolyte to be tested can be found in the microscopic chemical property library, then electrolyte molecule a belongs to the first electrolyte molecule. If the microscopic chemical property information of electrolyte molecule b in the electrolyte to be tested cannot be found, then electrolyte molecule b belongs to the second electrolyte molecule. The number of first electrolyte molecules can be 0, 1, or an integer greater than 1, and the number of second electrolyte molecules can be 0, 1, or an integer greater than 1.

[0044] Step 202: Determine the microscopic chemical properties of the first electrolyte molecule from the molecular microscopic chemical property library.

[0045] In other words, the microscopic chemical properties of the first electrolyte molecule can be obtained directly from the molecular microscopic chemical property database.

[0046] Step 203: Perform first-principles calculations based on the molecular structure of the second electrolyte molecules to obtain information on the microscopic chemical properties of the second electrolyte molecules.

[0047] Since the microscopic chemical properties of the second electrolyte molecules are still unknown, they need to be obtained through first-principles calculations. For details, please refer to the process of obtaining the microscopic chemical properties of multiple electrolyte molecules based on first-principles calculations in the above embodiments.

[0048] In other words, the molecular microchemical property library may not include all electrolyte molecules. For electrolyte molecules not included in the molecular microchemical property library, their microchemical property information needs to be calculated based on first-principles calculations.

[0049] In some embodiments, to improve the electrolyte performance prediction method, the molecular microscopic chemical property library can be continuously improved during the prediction process. Therefore, after performing step 203, the method may further include:

[0050] Step 204: Add the microscopic chemical property information of the second electrolyte molecules to the molecular microscopic chemical property library.

[0051] According to the electrolyte performance prediction method of this invention, based on a molecular microscopic chemical property library, a first electrolyte molecule and a second electrolyte molecule are identified among multiple electrolyte molecules. For the first electrolyte molecule, its microscopic chemical property information is directly determined from the molecular microscopic chemical property library. For the second electrolyte molecule, first-principles calculations are performed based on its molecular properties to obtain its microscopic chemical property information. This method significantly improves the efficiency of obtaining the microscopic chemical property information of electrolyte molecules within the electrolyte to be tested and avoids repeated first-principles calculations. Furthermore, by supplementing the molecular microscopic chemical property information of the second electrolyte molecule into the molecular microscopic chemical property library, the library can be continuously updated to continuously improve it and further enhance prediction efficiency.

[0052] Figure 3 This is the third flowchart illustrating the electrolyte performance prediction method provided in this embodiment of the invention. Figure 3 As shown, the electrolyte performance prediction model can be trained through the following steps:

[0053] Step 301: Determine the formula ratio sample corresponding to the electrolyte sample.

[0054] Among them, the electrolyte sample is an electrolyte with multiple formulations, such as all electrolytes with known properties, and the formulation ratio sample refers to the formulation ratio of each electrolyte in the electrolyte sample.

[0055] Step 302: Based on first-principles calculations, determine the microscopic chemical property information sample corresponding to the electrolyte sample.

[0056] Among them, the microchemical property information sample corresponding to the electrolyte sample includes the microchemical property information of electrolyte molecules in each electrolyte sample.

[0057] Step 303: Determine the performance label value of the electrolyte sample.

[0058] In some embodiments, the performance label values ​​of electrolyte samples can be based on experimental determination. To reduce testing costs, the performance label values ​​of electrolyte samples can also be obtained through simulation calculations, such as molecular dynamics simulations. The performance label values ​​of electrolyte samples are consistent with the data contained in the performance prediction results of the electrolyte under test output by the electrolyte performance prediction model.

[0059] Molecular dynamics simulations, using classical force fields, simulate the dynamic behavior of molecules in time and space, studying the stability, diffusion, and interfacial reactions of electrolyte molecules under different conditions, including ionic conductivity, viscosity, dielectric constant, melting point, and boiling point. Molecular dynamics can simulate the actual performance of electrolytes under varying temperatures, concentrations, and pressures, thus providing performance data without the need for experiments. However, since molecular dynamics simulations are time-consuming, to facilitate the application of electrolyte performance prediction methods, this invention allows performance label values ​​to be obtained during model training based on molecular dynamics simulations. This enables the electrolyte performance prediction model to learn the ability to predict electrolyte performance based on the electrolyte formulation ratio and the microscopic chemical properties of electrolyte molecules. In practical applications, performance prediction can be directly achieved through the electrolyte performance prediction model.

[0060] As one possible approach, the process of determining the performance label value of an electrolyte sample includes: performing molecular dynamics simulations based on the formulation ratio sample to obtain simulation results; and determining the performance label value based on the simulation results.

[0061] Specifically, the process of performing molecular dynamics simulation based on the formula ratio sample and obtaining the simulation results can be implemented based on relevant software, including: (1) constructing the simulation structure: based on the formula ratio, constructing a mixed simulation system with the corresponding ratio using Packmol software; (2) using Sobtop to generate a topology file of RESP2 (0.5) charge in the GAFF force field; (3) setting the simulation step size, number of steps, temperature, heat bath, bath pressure, etc.; (4) starting the molecular dynamics simulation. The molecular dynamics simulation process includes: (1) energy minimization, eliminating possible unreasonable conformations and interatomic conflicts in the molecular system to bring the system to a low energy state; (2) NVT equilibrium simulation, performing equilibrium simulation under constant particle number (N), volume (V) and temperature (T) conditions to bring the system to the target temperature; (3) NPT equilibrium simulation, performing equilibrium simulation under constant particle number (N), pressure (P) and temperature (T) conditions to bring the system to the target pressure and temperature; (4) production simulation, performing sufficient molecular dynamics simulation at the nanosecond level; (5) simulation at different temperatures, to simulate the performance of the electrolyte at different temperatures, modify the target temperature in the parameter file, and then repeat steps 1 to 4.

[0062] As an example, the performance label values ​​are determined based on the simulation results, including: (1) Calculating the mean square displacement (MSD) of the system based on the simulation results, calculating the diffusion coefficient based on the msd tool of GROMACS, and calculating the diffusion coefficient based on the Einstein relation, and then calculating the viscosity through the Stokes-Einstein equation; (2) Calculating the ionic conductivity by calculating the diffusion coefficient of the particles and combining it with the Stokes-Einstein equation; (3) Determining the melting point by analyzing the change curves of density, energy, etc. with temperature in the simulation results.

[0063] Step 304: Based on the training samples determined by the formula ratio sample, the microscopic chemical property information sample, and the performance label value, train the initial electrolyte performance prediction model to obtain the trained electrolyte performance prediction model.

[0064] During the training process, the formula ratio samples and microscopic chemical property information samples are input into the initial electrolyte performance prediction model to obtain the performance prediction results output by the initial electrolyte performance prediction model. Based on the performance prediction results and performance label values, the loss value is calculated, and the model parameters are continuously adjusted based on the loss value until the trained electrolyte performance prediction model is obtained.

[0065] According to the electrolyte performance prediction method of the present invention, when training the electrolyte performance prediction model, the performance label value of the electrolyte sample is determined by molecular dynamics simulation, which can greatly reduce the experimental cost required for the training process.

[0066] In order to apply the electrolyte performance prediction method of the above embodiments to the actual electrolyte research and development process, the present invention also provides a method for determining electrolyte formulation.

[0067] Figure 4 This is a schematic flowchart illustrating the method for determining the electrolyte formulation provided in an embodiment of the present invention. Figure 4 As shown, the method may include the following steps:

[0068] Step 401: Based on the above electrolyte performance prediction method, obtain the performance prediction results of electrolytes with different formulations.

[0069] In the actual electrolyte development process, it is necessary to study electrolytes with different formulations. If traditional experimental methods are used, it will consume a lot of time and cost. Therefore, the electrolyte performance prediction method in the above embodiment can be used to input the formulation ratio of each electrolyte and the microscopic chemical properties of the electrolyte molecules into the electrolyte performance prediction model to obtain the performance prediction results of each electrolyte formulation. This can greatly improve the development efficiency and reduce the experimental cost.

[0070] Step 402: Based on the performance prediction results, determine the target electrolyte formulation from different formulations.

[0071] The target electrolyte formulation refers to an electrolyte formulation with superior performance. There can be one or more target electrolyte formulations.

[0072] In some embodiments, a performance threshold range can be preset based on actual needs. If the performance prediction result of an electrolyte formulation is within the performance threshold range, then the formulation is the target electrolyte formulation. If the performance prediction result includes multiple performance indicators, the performance threshold range can include the threshold range of each of the multiple performance indicators. If the prediction result of each performance indicator in the performance prediction result of an electrolyte formulation is within its corresponding performance threshold range, then the formulation is determined to be the target electrolyte formulation.

[0073] To improve the accuracy of target electrolyte formulation determination, the process of determining the target electrolyte formulation from different formulations based on performance prediction results may include the following steps:

[0074] Step S1: Based on the performance prediction results, determine the first target electrolyte formulation from different formulations; wherein, the first target electrolyte formulation is the formulation corresponding to the electrolyte whose performance prediction results meet the preset conditions.

[0075] The preset condition is the aforementioned performance threshold range. If the performance prediction result of an electrolyte formulation is within the performance prediction range, then the formulation is determined to be the first target electrolyte formulation.

[0076] Step S2: Based on the molecular dynamics simulation results of the first target electrolyte formulation, verify the performance prediction results of the first target electrolyte formulation.

[0077] In other words, based on the first target electrolyte formulation, a simulated mixing system is established, and molecular dynamics simulation is performed on the mixing system to obtain the performance simulation results corresponding to the first target electrolyte formulation. Based on the performance simulation results, the performance prediction results are verified to ensure the accuracy of the target electrolyte formulation determination.

[0078] Step S3: If the verification is successful, the first target electrolyte formulation is determined as the target electrolyte formulation.

[0079] In some embodiments, if the performance simulation results of the first target electrolyte formulation are within a preset performance threshold range, the verification is considered successful, and the first target electrolyte formulation is determined to be the target electrolyte formulation. If the performance simulation results of the first target electrolyte formulation are not within the preset performance threshold range, the verification is considered unsuccessful, meaning the first target electrolyte formulation cannot be the target electrolyte formulation.

[0080] The electrolyte formulation determination method of this invention, on the one hand, applies electrolyte performance prediction methods to the electrolyte formulation determination process, which can greatly improve R&D efficiency and reduce experimental costs; on the other hand, by combining molecular dynamics simulation to verify the screening of electrolyte formulations, the accuracy of the target electrolyte formulation determination can be guaranteed.

[0081] To achieve the above embodiments, the present invention also provides an electrolyte performance prediction device.

[0082] Figure 5 This is a schematic diagram of the electrolyte performance prediction device provided in an embodiment of the present invention. Figure 5 As shown, the electrolyte performance prediction device of this embodiment may include: a first determining module 501, a second determining module 502, and a prediction module 503. The first determining module 501 is used to determine the formulation ratio of the electrolyte to be tested; the second determining module 502 is used to determine the microscopic chemical properties of each of the multiple electrolyte molecules in the electrolyte to be tested based on first-principles calculations; the prediction module 503 is used to input the microscopic chemical properties of each of the multiple electrolyte molecules and the formulation ratio into an electrolyte performance prediction model to obtain the performance prediction result of the electrolyte to be tested output by the electrolyte performance prediction model; wherein the electrolyte performance prediction model has learned the ability to predict the performance of an electrolyte formulation based on the electrolyte formulation ratio and the microscopic chemical properties of the electrolyte molecules.

[0083] In some embodiments, the second determining module 502 is specifically used to: determine a first electrolyte molecule and a second electrolyte molecule among the plurality of electrolyte molecules; the microscopic chemical property information of the first electrolyte molecule is included in a molecular microscopic chemical property library; the microscopic chemical property information of the second electrolyte molecule is not included in the molecular microscopic chemical property library; the molecular microscopic chemical property library includes the microscopic chemical property information of each of the plurality of calculated electrolyte molecules, and the microscopic chemical property information of each of the calculated electrolyte molecules is obtained by first-principles calculation based on the molecular structure of the electrolyte molecule; determine the microscopic chemical property information of the first electrolyte molecule from the molecular microscopic chemical property library; and obtain the microscopic chemical property information of the second electrolyte molecule by performing first-principles calculation based on the molecular structure of the second electrolyte molecule.

[0084] In some embodiments, the second determining module 502 is further configured to: supplement the molecular microchemical property information of the second electrolyte molecules to the molecular microchemical property library.

[0085] In some embodiments, the device further includes a training module 504, which is configured to: determine a formulation ratio sample corresponding to an electrolyte sample; determine a microscopic chemical property information sample corresponding to the electrolyte sample based on the first principle calculation method; determine a performance label value of the electrolyte sample; and train an initial electrolyte performance prediction model based on the formulation ratio sample, the microscopic chemical property information sample, and the training sample determined by the performance label value to obtain a trained electrolyte performance prediction model.

[0086] In some embodiments, the training module 504 is further configured to: perform molecular dynamics simulations based on the formulation ratio sample to obtain simulation results; and determine the performance label value based on the simulation results.

[0087] According to an embodiment of the present invention, an electrolyte performance prediction device includes a first determining module for determining the formulation ratio of the electrolyte to be tested; a second determining module for determining the microscopic chemical properties of multiple electrolyte molecules within the electrolyte to be tested based on first-principles calculations; and a prediction module for inputting the microscopic chemical properties of the multiple electrolyte molecules and the formulation ratio into an electrolyte performance prediction model to obtain the performance prediction result of the electrolyte to be tested output by the electrolyte performance prediction model. The electrolyte performance prediction model has learned to predict electrolyte performance based on the electrolyte formulation ratio and the microscopic chemical properties of the electrolyte molecules. This invention combines first-principles calculations and an electrolyte performance prediction model, enabling electrolyte performance prediction without traditional experimental procedures. This not only improves the efficiency of electrolyte performance prediction and reduces experimental costs but also ensures the accuracy of electrolyte performance prediction, thereby shortening the electrolyte development cycle and reducing development costs. Furthermore, the microscopic chemical properties obtained from first-principles calculations can be used to explain electrolyte performance at the microscopic mechanism level, enhancing the transparency and credibility of the technology.

[0088] It should be noted that the explanations and descriptions of the electrolyte performance prediction method in the above embodiments also apply to the electrolyte performance prediction device in the embodiments of the present invention, and will not be repeated here.

[0089] Figure 6 This is a schematic diagram of the structure of the electrolyte formulation determination device provided in an embodiment of the present invention. Figure 6 As shown, the electrolyte formulation determination device of this embodiment may include: an acquisition module 601, used to obtain the performance prediction results of electrolytes with different formulations based on the above-described electrolyte performance prediction method; and a determination module 602, used to determine the target electrolyte formulation from the plurality of formulations based on the performance prediction results.

[0090] In some embodiments, the determining module 602 is specifically used to: determine a first target electrolyte formulation from the different formulations based on the performance prediction results; wherein the first target electrolyte formulation is the formulation corresponding to an electrolyte whose performance prediction results meet preset conditions; verify the performance prediction results of the first target electrolyte formulation based on the molecular dynamics simulation results of the first target electrolyte; if the verification is successful, determine the first target electrolyte as the target electrolyte formulation.

[0091] The electrolyte formulation determination apparatus according to embodiments of the present invention, on the one hand, applies the electrolyte performance prediction method to the electrolyte formulation determination process, which can greatly improve R&D efficiency and reduce experimental costs; on the other hand, by combining molecular dynamics simulation to verify the screening of electrolyte formulations, the accuracy of the determination of the target electrolyte formulation can be guaranteed.

[0092] It should be noted that the explanation of the method for determining the electrolyte formula in the above embodiments also applies to the electrolyte formula determination device in the embodiments of the present invention, and will not be repeated here.

[0093] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 may call the computer program in the memory 730 to execute the steps of the electrolyte performance prediction method provided in the above embodiments, and / or the steps of the electrolyte formulation determination method provided in the above embodiments.

[0094] For example, the method includes: determining the formulation ratio of the electrolyte to be tested; determining the microscopic chemical properties of each of multiple electrolyte molecules in the electrolyte to be tested based on first-principles calculations; inputting the microscopic chemical properties of each of the multiple electrolyte molecules and the formulation ratio into an electrolyte performance prediction model to obtain the performance prediction result of the electrolyte to be tested output by the electrolyte performance prediction model; wherein, the electrolyte performance prediction model has learned the ability to predict electrolyte performance based on the electrolyte formulation ratio and the microscopic chemical properties of electrolyte molecules; and / or,

[0095] The method includes: obtaining performance prediction results for electrolytes with different formulations based on the electrolyte performance prediction method of the above embodiments; and determining a target electrolyte formulation from the different formulations based on the performance prediction results.

[0096] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] On the other hand, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, the computer program being stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to perform the steps of the electrolyte performance prediction method provided in the above embodiments, and / or the steps of the electrolyte formulation determination method provided in the above embodiments.

[0098] On the other hand, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program, the computer program being used to cause a processor to execute the electrolyte performance prediction method and / or the electrolyte formulation determination method provided in the above embodiments.

[0099] The non-transitory computer-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0100] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting electrolyte performance, characterized in that, include: Determine the formulation ratio of the electrolyte to be tested; Based on first-principles calculations, the microscopic chemical properties of each of the multiple electrolyte molecules in the electrolyte to be tested are determined. The microscopic chemical properties of each of the multiple electrolyte molecules and the formulation ratio are input into the electrolyte performance prediction model to obtain the performance prediction result of the electrolyte to be tested output by the electrolyte performance prediction model; wherein, the electrolyte performance prediction model has learned the ability to predict the performance of the electrolyte based on the formulation ratio and the microscopic chemical properties of the electrolyte molecules.

2. The method according to claim 1, characterized in that, The method of determining the microscopic chemical properties of multiple electrolyte molecules within the electrolyte under test using first-principles calculations includes: The first electrolyte molecule and the second electrolyte molecule are identified from the plurality of electrolyte molecules; the microscopic chemical properties of the first electrolyte molecule are included in a molecular microscopic chemical property library; the microscopic chemical properties of the second electrolyte molecule are not included in the molecular microscopic chemical property library; the molecular microscopic chemical property library includes the microscopic chemical properties of each of the plurality of calculated electrolyte molecules, and the microscopic chemical properties of each of the calculated electrolyte molecules are obtained by first-principles calculations based on the molecular structure of the electrolyte molecule; The microscopic chemical properties of the first electrolyte molecules are determined from the molecular microscopic chemical property library. First-principles calculations were performed based on the molecular structure of the second electrolyte molecules to obtain information on the microscopic chemical properties of the second electrolyte molecules.

3. The method according to claim 2, characterized in that, Also includes: The microscopic chemical properties information of the second electrolyte molecules are added to the molecular microscopic chemical property library.

4. The method according to claim 1, characterized in that, The electrolyte performance prediction model was trained in the following way: Determine the formula ratio sample corresponding to the electrolyte sample; Based on the calculation method of the first principle, the microscopic chemical property information sample corresponding to the electrolyte sample is determined; Determine the performance label value of the electrolyte sample; Based on the formula ratio sample, the microscopic chemical property information sample, and the training sample determined by the performance label value, the initial electrolyte performance prediction model is trained to obtain the trained electrolyte performance prediction model.

5. The method according to claim 4, characterized in that, Determining the performance label value of the electrolyte sample includes: Based on the aforementioned formula ratio sample, molecular dynamics simulations were performed to obtain simulation results; The performance label value is determined based on the simulation results.

6. A method for determining an electrolyte formulation, characterized in that, include: Based on the electrolyte performance prediction method according to any one of claims 1 to 5, the performance prediction results of electrolytes with different formulations are obtained. Based on the performance prediction results, the target electrolyte formulation is determined from the different formulations.

7. The method according to claim 6, characterized in that, The step of determining the target electrolyte formulation from the different formulations based on the performance prediction results includes: Based on the performance prediction results, a first target electrolyte formulation is determined from the different formulations; wherein, the first target electrolyte formulation is the formulation corresponding to the electrolyte whose performance prediction results meet preset conditions; Based on the molecular dynamics simulation results of the first target electrolyte formulation, the performance prediction results of the first target electrolyte formulation are verified. If the verification is successful, the first target electrolyte formulation will be determined as the target electrolyte formulation.

8. An electrolyte performance prediction device, characterized in that, include: The first determining module is used to determine the formulation ratio of the electrolyte to be tested; The second determining module is used to determine the microscopic chemical properties of each of the multiple electrolyte molecules in the electrolyte to be tested based on first-principles calculations. The prediction module is used to input the microscopic chemical properties of each of the multiple electrolyte molecules and the formulation ratio into the electrolyte performance prediction model to obtain the performance prediction result of the electrolyte to be tested output by the electrolyte performance prediction model; wherein, the electrolyte performance prediction model has learned the ability to predict the performance of the electrolyte formulation based on the formulation ratio and the microscopic chemical properties of the electrolyte molecules.

9. An apparatus for determining an electrolyte formulation, characterized in that, include: The acquisition module is used to obtain the performance prediction results of electrolytes with different formulations based on the electrolyte performance prediction method according to any one of claims 1 to 5. A determination module is used to determine a target electrolyte formulation from the plurality of formulations based on the performance prediction results.

10. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the electrolyte performance prediction method according to any one of claims 1 to 5, and / or implements the electrolyte formulation determination method according to claim 6 or 7.

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