CN114255826A

CN114255826A - Electrolyte design method, device, equipment, medium and program product

Info

Publication number: CN114255826A
Application number: CN202111492691.8A
Authority: CN
Inventors: 陈翔; 张强; 姚楠
Original assignee: Tsinghua University
Current assignee: Tsinghua University
Priority date: 2021-12-08
Filing date: 2021-12-08
Publication date: 2022-03-29
Also published as: WO2023103150A1

Abstract

The application discloses an electrolyte design method, device, equipment, medium and program product. The method comprises the following steps: acquiring a first physical and chemical parameter of an electrolyte to be designed; inputting the first physical and chemical parameters into a trained component prediction model, and predicting component information of the electrolyte to be designed through the component prediction model to obtain the component information of the electrolyte to be designed, wherein the component prediction model is obtained through training of a training sample obtained based on a high-throughput calculation method; and designing the electrolyte to be designed according to the component information of the electrolyte to be designed. According to the embodiment of the application, the electrolyte design with low cost and high efficiency can be realized.

Description

Electrolyte design method, device, equipment, medium and program product

Technical Field

The present application relates to the field of secondary battery technology, and in particular, to an electrolyte design method, apparatus, device, computer storage medium, and computer program product.

Background

In recent years, with the increasing application range of secondary batteries represented by lithium ion batteries, lithium ion batteries are widely used in energy storage power systems such as hydraulic power, thermal power, wind power, and solar power stations, and in a plurality of fields such as electric tools, electric bicycles, electric motorcycles, electric automobiles, military equipment, and aerospace. As the secondary ion battery has been greatly developed, higher requirements are also placed on its energy density, cycle performance, safety performance, and the like.

The electrolyte is an important component of the secondary battery, and plays roles of conducting active ions and insulating electrons. The positive and negative active materials determine the theoretical capacity of the secondary battery, but the charge-discharge rate, the long cycle performance and other properties of the secondary battery are closely related to the electrolyte. From the development process of lithium ion battery technology, the selection of the electrolyte system largely determines the success of the secondary battery. Therefore, the development of advanced electrolytes is one of the core of the development of next-generation secondary battery technology.

However, because the electrolyte has complex components, a large amount of work is often required to design the electrolyte meeting the actual requirements.

Disclosure of Invention

Embodiments of the present application provide an electrolyte design method, apparatus, device, computer storage medium, and computer program product, which can implement efficient and low-cost design of an electrolyte.

In a first aspect, an embodiment of the present application provides an electrolyte solution design method, including:

acquiring a first physical and chemical parameter of an electrolyte to be designed;

inputting the first physical and chemical parameters into a trained component prediction model, and predicting component information of the electrolyte to be designed through the component prediction model to obtain the component information of the electrolyte to be designed, wherein the component prediction model is obtained through training of a training sample obtained based on a high-throughput calculation method;

and designing the electrolyte to be designed according to the component information of the electrolyte to be designed.

In some embodiments of the first aspect, before inputting the first physicochemical parameter into the trained component prediction model, and predicting the component information of the electrolyte to be designed through the component prediction model to obtain the component information of the electrolyte to be designed, the method further includes:

simulating a plurality of first electrolytes with different component information through a pre-trained simulation model;

calculating the first electrolyte by using a high-throughput calculation method to obtain a second physicochemical parameter corresponding to the first electrolyte;

respectively establishing a training sample according to the second physicochemical parameter of each first electrolyte and the component information of each first electrolyte;

and training the component prediction model according to the plurality of training samples until the training stopping condition is met, and obtaining the trained component prediction model.

In some embodiments of the first aspect, training the component prediction model based on a plurality of training samples until a training stop condition is satisfied, resulting in a trained component prediction model, comprises:

for each training sample, the following steps are respectively carried out:

inputting the training samples into a preset component prediction model to obtain predicted component information corresponding to the second physical and chemical parameters;

determining a loss function value of the component prediction model according to the predicted component information and the component information of the first electrolyte;

and under the condition that the loss function value does not meet the training stopping condition, adjusting the model parameters of the component prediction model, and training the component prediction model after parameter adjustment by using the training sample until the training stopping condition is met to obtain the trained component prediction model.

In some embodiments of the first aspect, the high-throughput computing method comprises at least one of density functional theory calculations, molecular dynamics simulations, and finite element calculations.

In some embodiments of the first aspect, the first physicochemical parameter includes at least one of a physicochemical parameter of solvent molecules constituting the electrolyte and a physicochemical parameter of the electrolyte.

In some embodiments of the first aspect, the physicochemical parameter of the solvent molecule constituting the electrolyte solution includes at least one of a geometry, an electronic structure, thermodynamic energy, a molecular pre-linear orbital level, a dipole moment, an electron affinity, an oxidation-reduction potential, a raman spectrum, an infrared spectrum, a dielectric constant, a viscosity, a melting point, and a boiling point of the solvent molecule.

In some embodiments of the first aspect, the physico-chemical parameter of the electrolyte comprises at least one of a dielectric constant, a viscosity, a melting point, a boiling point, an ion conductivity of the electrolyte.

In some embodiments of the first aspect, the compositional information comprises a composition of matter and a ratio.

In some embodiments of the first aspect, the component prediction model comprises at least one of a linear regression analysis model, a logistic regression analysis model, a support vector machine, a nearest neighbor algorithm model, a K-means clustering algorithm model, a decision tree model, a naive bayes model, a random forest model, a dimension reduction algorithm model, a gradient enhancement algorithm model.

In some embodiments of the first aspect, the electrolyte to be designed includes an aqueous electrolyte and a non-aqueous electrolyte.

In some embodiments of the first aspect, the electrolyte is designed to include a solvent, an electrolyte salt, and optionally additives,

wherein the solvent comprises one or more organic small molecules, and the organic small molecules consist of at least two elements of C, H, O, N, F, Cl, S, P and B;

the electrolyte salt includes at least one of hexafluorophosphate, bifluorosulfonylimide, nitrate, nitrite, fluoride, chloride, bromide, iodide, difluorophosphate, difluorooxalato borate, dioxaoxalato borate, tetrafluorooxalato borate, bifluorosulfonylimide, bistrifluoromethylsulfonylimide, 4, 5-dicyano-2-trifluoromethylimidazolium, perchlorate, sulfate, sulfite, hexafluoroarsenate, and the cation in the electrolyte salt is selected from Li⁺、Na⁺、K⁺、Mg²⁺、Ca²⁺、Zn²⁺、Al³⁺At least one of (a);

the optional additives include at least one of a film forming additive, a flame retardant additive, an anti-overcharge additive, an overcharge protection additive, and a multifunctional additive.

In a second aspect, an embodiment of the present application provides an electrolyte solution designing apparatus, including:

the acquisition module is used for acquiring a first physical and chemical parameter of the electrolyte to be designed;

the prediction module is used for inputting the first physical and chemical parameters into the trained component prediction model, predicting the component information of the electrolyte to be designed through the component prediction model, and obtaining the component information of the electrolyte to be designed, wherein the component prediction model is obtained through training of a training sample obtained based on a high-throughput calculation method;

and the design module is used for designing the electrolyte to be designed according to the component information of the electrolyte to be designed.

In a third aspect, an embodiment of the present application provides an electrolyte designing apparatus, including: a processor and a memory storing computer program instructions;

a processor, when executing the computer program instructions, implements the electrolyte design method as in any embodiment of the first aspect of the present application.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method for designing an electrolyte according to any one of the embodiments of the first aspect of the present application is implemented.

In a fifth aspect, embodiments of the present application provide a computer program product, where instructions of the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the electrolyte design method according to any of the embodiments of the first aspect of the present application.

According to the electrolyte design method, the electrolyte design device, the electrolyte design equipment, the computer storage medium and the computer program product, the component information corresponding to the physical and chemical parameters can be obtained by inputting the physical and chemical parameters of the electrolyte into the component prediction model, so that the components of the electrolyte can be designed according to the performance requirements of the electrolyte without carrying out actual experimental work, and the electrolyte meeting the performance requirements can be obtained. In addition, because the training sample of the component prediction model is obtained based on a high-throughput calculation method, a large amount of experiments are not needed to obtain the training sample, and the experiment cost is reduced. The embodiment of the application can realize the design of the electrolyte with low cost and high efficiency.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic flow diagram of an electrolyte design method provided in one embodiment of the present application;

FIG. 2 is a schematic flow diagram of an electrolyte design method provided in another embodiment of the present application;

FIG. 3 is a schematic structural view of an electrolyte design apparatus provided in accordance with yet another embodiment of the present application;

fig. 4 is a schematic structural diagram of an electrolyte designing apparatus according to still another embodiment of the present disclosure.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

As described in the background art, in the prior art, a large number of experiments are often required to design the electrolyte, for example, researchers are required to design the material composition and the mixture ratio of a plurality of different electrolytes according to experience, and then verify the performance of the electrolyte, or optimize the formula based on the existing electrolyte. The inventor finds that the electrolyte design method in the prior art has long research and development period, high cost and poor mobility, and the internal rules of electrolyte design and development are difficult to summarize.

In the biological field, the High Throughput Screening (HTS) technology is a technical system that is based on molecular level and cell level experimental methods, uses a microplate form as an experimental tool carrier, uses an automatic operating system to execute an experimental process, uses a sensitive and rapid detection instrument to collect experimental result data, uses a computer to analyze and process the experimental data, detects tens of millions of samples at the same time, and uses an obtained corresponding database to support operation, and has the characteristics of trace, rapidness, sensitivity, accuracy and the like. In short, a large amount of information can be obtained through one experiment, and valuable information can be found from the information.

The inventor finds that the high-throughput calculation method based on high-throughput screening is applied to electrolyte design, so that the experiment cost can be effectively reduced, and the internal relation between the electrolyte component information and the performance can be obtained.

In order to solve the problems of the prior art, embodiments of the present application provide an electrolyte design method, apparatus, device, computer storage medium, and computer program product.

The electrolyte design method provided by the embodiment of the present application is described below with reference to the accompanying drawings by specific embodiments and application scenarios thereof.

Fig. 1 shows a schematic flow chart of an electrolyte design method provided in an embodiment of the present application, and in particular, may be a method for training a composition prediction model used in the electrolyte design method provided in the embodiment of the present application.

As shown in fig. 1, the composition prediction model used in the electrolyte design method provided in the embodiment of the present application may include steps S110 to S140.

And S110, simulating a plurality of first electrolytes with different component information through a pre-trained simulation model.

And S120, calculating the first electrolyte by using a high-throughput calculation method to obtain a second physicochemical parameter corresponding to the first electrolyte.

And S130, respectively creating a training sample according to the second physicochemical parameter of each first electrolyte and the composition information of each first electrolyte.

And S140, training the component prediction model according to the plurality of training samples until the training stopping condition is met, and obtaining the trained component prediction model.

In the embodiment of the application, a plurality of electrolytes with different component information are simulated through a simulation model, then the electrolytes simulated through the simulation model are calculated through a high-throughput calculation method to obtain second physicochemical parameters corresponding to the first electrolytes, and the component information of each first electrolyte and the corresponding second physicochemical parameters are respectively used for constructing training samples, so that the component prediction model can be trained to obtain the trained component prediction model. Then, the physicochemical parameters of the electrolyte to be designed can be processed based on the trained component prediction model, so that the component information of the electrolyte to be designed is obtained, and the electrolyte is designed according to the component information of the electrolyte to be designed, so that the low-cost and high-efficiency design of the electrolyte is realized.

The following describes the training process of the component prediction model in detail.

First, step S110 is described, in which a plurality of first electrolytes having different composition information are simulated by a pre-trained simulation model.

The simulation model may be a simulation model commonly used in the art, for example, a simulation model constructed and trained by at least one of an analog computer, a digital computer, and a hybrid computer. As an example, the simulation model may be a simulation model constructed and trained by a simulation computer, and specifically, an electrolyte calculation workflow having multiple scales of material composition, material proportion, and the like may be obtained by the simulation model, so as to simulate a plurality of electrolytes having different composition information.

In an alternative embodiment, the first electrolyte may include an aqueous electrolyte and a non-aqueous electrolyte. As one example, the first electrolyte may include an electrolyte using water as a solvent and an organic electrolyte using an organic substance as a solvent. In an alternative embodiment, the first electrolyte may comprise an electrolyte used in a lithium ion battery, a lithium metal battery, a sodium ion battery, a sodium metal battery, a potassium ion battery, a potassium metal battery, a magnesium ion battery, a magnesium metal battery, a calcium ion battery, a calcium metal battery, a zinc ion battery, a zinc metal battery, an aluminum ion battery, an aluminum metal battery.

In an alternative embodiment, the first electrolyte solution may include a solvent, an electrolyte salt, and optionally an additive.

Wherein, the solvent can comprise one or more organic small molecules, and the organic small molecules are composed of at least two elements of C, H, O, N, F, Cl, S, P and B.

The electrolyte salt may include at least one of hexafluorophosphate, bisfluorosulfonylimide, nitrate, nitrite, fluoride, chloride, bromide, iodide, difluorophosphate, difluorooxalato borate, dioxaoxalato borate, tetrafluorooxalato borate, bisfluorosulfonylimide, bistrifluoromethylsulfonylimide, 4, 5-dicyano-2-trifluoromethylimidazolium, perchlorate, sulfate, sulfite, hexafluoroarsenate, and a cation in the electrolyte salt is selected from Li⁺、Na⁺、K⁺、Mg²⁺、Ca²⁺、Zn²⁺、Al³⁺At least one of (a).

The optional additives may include at least one of a film forming additive, a flame retardant additive, an anti-overcharge additive, an overcharge protection additive, and a multifunctional additive.

The composition information may be information related to the composition of the electrolytic solution. In an alternative embodiment, the composition information may include the composition and proportion of the substances in the electrolyte.

A simulation model is used for simulating the multiple first electrolytes with different component information, so that a large amount of comprehensive information related to the electrolytes can be obtained, and a more comprehensive training sample can be obtained in the subsequent steps. Thus, the trained component prediction model can be suitable for the design of various secondary battery electrolytes, and the applicability of the component prediction model is improved.

Then, step S120 is introduced, and the first electrolyte is calculated by using a high-throughput calculation method to obtain a second physicochemical parameter corresponding to the first electrolyte.

In step S120, the second physicochemical parameter corresponding to the first electrolyte is a parameter of a physical property and a chemical property related to the performance of the first electrolyte.

In an alternative embodiment, the second physicochemical parameter may include at least one of a physicochemical parameter of solvent molecules constituting the electrolyte and a physicochemical parameter of the electrolyte.

Specifically, the physicochemical parameters of the solvent molecules constituting the electrolyte may include those of the solvent molecules in a non-solution environment, such as those in a vacuum environment, and may also include those of the solvent molecules in a solution environment, such as those in the electrolyte. In an alternative embodiment, the physical and chemical parameters of the solvent molecules constituting the electrolyte may include at least one of a geometry, an electronic structure, thermodynamic energy, a molecular front orbital level, a dipole moment, an electron affinity, an oxidation-reduction potential, a raman spectrum, an infrared spectrum, a dielectric constant, a viscosity, a melting point, and a boiling point of the solvent molecules.

In an alternative embodiment, the physical and chemical parameters of the electrolyte may include at least one of a dielectric constant, a viscosity, a melting point, a boiling point, and an ion conductivity of the electrolyte.

The high-throughput computing method described above may include a variety of computing methods. In an alternative embodiment, the high-throughput computing method includes at least one of density functional theory calculations, molecular dynamics simulations, and finite element calculations.

For ease of understanding, some implementations of the high throughput computing method in the present application are exemplified below. It should be noted that the following examples are only for explaining the present application, and are not intended to limit the present application.

As an example of calculating physicochemical parameters of electrolyte solvent molecules in a vacuum environment by adopting a density functional theory, for 1, 2-Dimethoxyethane (DME) molecules, constructing a DME molecule initial geometric configuration by using GaussView software; then, Gaussian16 software can be adopted to optimize the constructed DME molecule initial geometric configuration and calculate related physicochemical properties on an optimal structure, and the adopted specific method is a density functional theory, the basis set is 6-311+ + G (d, p), and the functional is B3 LYP; then, the optimized structure obtained by adopting vibration frequency analysis is determined to be a ground state structure, and the charge distribution of DME molecules is analyzed by adopting NBO charges; and finally, analyzing and optimizing physical and chemical parameters of the geometry structure (bond length, bond angle and dihedral angle), the electronic structure (NBO charge distribution), thermodynamic energy, electron affinity energy, dipole moment, Raman spectrum, infrared spectrum, Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) energy levels, oxidation-reduction potential and the like of the obtained DME molecules.

As an example of calculating the dielectric constant and viscosity of an electrolyte solvent using molecular dynamics simulation, for 1, 3-Dioxolane (DOL), a DOL solvent model containing a plurality of solvent molecules may be constructed using Packmol software, for example, a DOL solvent model containing 1000 solvent molecules may be constructed; the molecular dynamics simulation process that can then be performed on the DOL solvent model using LAMMPS software can include the following steps (1) - (4).

(1) Carrying out isothermal isobaric ensemble (NPT ensemble) calculation at 298K, wherein the pressure is 1 atmosphere, the time is 3ns, and the time step is 1 fs;

(2) heating from 298K to 400K, the ensemble is NPT, the pressure is 1 atmosphere, the heating rate is 20K/ns, and finally, keeping the temperature at 400K for 2ns, and the time step is 1 fs.

(3) The temperature is reduced from 400K to 298K, the ensemble is NPT, the pressure is 1 atmosphere, the temperature reduction rate is 20K/ns, and finally the temperature is kept for 5ns at 298K, and the time step is 1 fs.

(4) A regular ensemble (NVT ensemble) calculation is performed at 298K, with a time of 10ns and a time step of 1 fs.

Performing molecular dynamics simulation on the second half of the calculated track based on NVT, and calculating the dielectric constant and viscosity of the DOL solvent, wherein the dielectric constant calculation formula is as follows:

wherein ε represents a dielectric constant ∈₀Is a dielectric constant of a vacuum, and,

is the total dipole moment of the system, T is the temperature, V is the volume of the electrolyte, k_BIs the boltzmann constant.

The viscosity is calculated by the formula:

where eta is viscosity, T is temperature, V is volume of electrolyte, k_BIs the Boltzmann constant, P_xzIs the system pressure.

As an example of calculating physicochemical parameters of solvent molecules in an electrolyte under a solution environment by adopting a density functional theory, for dimethyl carbonate (DMC) molecules, constructing an initial geometric configuration of the DMC molecules by adopting GaussView software; then, Gaussian16 software can be adopted to optimize the constructed DMC molecule initial geometric configuration, relevant physicochemical properties are calculated on an optimal structure, the adopted specific method is a density functional theory, the basis set is 6-311+ + G (d, p), the functional is B3LYP, an implicit solvation model (SMD solvation model) is adopted to describe the solution environment, and the dielectric constant of the SMD solvation model can be obtained through molecular dynamics simulation calculation; then, the obtained optimized structure can be determined to be a ground state structure by adopting vibration frequency analysis, and the charge distribution of DMC molecules is analyzed by adopting NBO charge; and finally, analyzing and optimizing physical and chemical parameters of the DMC molecule, such as geometric structure (bond length, bond angle and dihedral angle), electronic structure (NBO charge distribution), thermodynamic energy, electron affinity energy, dipole moment, Raman spectrum, infrared spectrum, HOMO and LUMO energy levels, oxidation-reduction potential and the like. By combining the physical and chemical parameters of the electrolyte solvent molecules in the vacuum environment calculated by adopting the density functional theory, the difference of the physical and chemical parameters of the solvent molecules in the vacuum environment and the solution environment can be compared, and the internal relation between the electrolyte solvent environment and the physical and chemical parameters of the solvent molecules in the electrolyte is explored based on the difference of the physical and chemical parameters.

As an example of calculating the physicochemical parameter of the ion-solvent structure formed by electrolyte solvent molecules and cations in a solution environment by using the density functional theory, Propylene Carbonate (PC) molecules and Li⁺Firstly, the PC molecule and Li can be constructed by GaussView software⁺Formed Li⁺-an ionic-solvent initial structure of PC, in particular, a constructed initial structure comprising PC molecules with Li⁺All possible structures formed; gaussian16 software can then be used to align the constructed Li⁺Optimizing an ion-solvent initial structure of the PC, and calculating related physicochemical properties on the optimal structure by adopting a specific method of a density functional theory, wherein the basis set is 6-311+ + G (d, p), the functional is B3LYP, describing a solution environment by adopting an SMD (surface mounted device) solvation model, and setting the dielectric constant of the SMD solvation model by molecular dynamics simulation calculation; the resulting optimized structure may then be determined to be the ground state structure using vibration frequency analysis, and Li may be analyzed using NBO charge⁺-charge distribution of PC; and finally, analyzing and optimizing physical and chemical parameters of the obtained PC molecule, such as geometric structure (bond length, bond angle and dihedral angle), electronic structure (NBO charge distribution), thermodynamic energy, electron affinity energy, dipole moment, Raman spectrum, infrared spectrum, HOMO and LUMO energy levels, oxidation-reduction potential and the like. Combined with the use of secretThe physicochemical parameters of the electrolyte solvent molecules in the solution environment obtained by the calculation of the degree functional theory can be used for comparing the PC molecules with the Li molecules⁺The difference of physicochemical parameters before and after the action is based on the research of the internal relation between the interaction between the solvent molecules and the cations in the electrolyte and the physicochemical parameters of the solvent molecules.

As an example of calculating physicochemical parameters of an electrolyte by using molecular dynamics simulation, for a lithium bistrifluoromethanesulfonylimide (LiTFSI) DOL/DME electrolyte, firstly, a solvent model containing a plurality of DOL solvent molecules and DME solvent molecules can be constructed by using Packmol software, specifically, a solvent model containing 500 DOL molecules and 500 DME molecules can be constructed, and 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 LiTFSI molecules are respectively added into the solvent model to obtain a LiTFSI DOL/DME electrolyte model; molecular dynamics simulation can then be performed on the LiTFSIDOL/DME electrolyte model by using LAMMPS software, and the simulation process can comprise the following steps (1) to (4).

(1) NPT ensemble calculation is carried out under 298K, the pressure is 1 atmosphere, the time is 3ns, and the time step is 1 fs;

(3) The temperature is reduced from 400K to 298K, the ensemble is NPT, the pressure is 1 atmosphere, the temperature reduction rate is 20K/ns, and finally the temperature is kept at 298K for 5ns, and the time step is 1 fs.

(4) NVT ensemble calculations are performed at 298K, with a time of 10ns, with a time step of 1 fs.

Performing molecular dynamics simulation on the second half of the NVT calculation track, and calculating the dielectric constant, viscosity and ion conductivity of the DOL/DME solvent, wherein the dielectric constant calculation formula is as follows:

The viscosity is calculated by the formula:

where eta is viscosity, T is temperature, V is volume, k_BIs the Boltzmann constant, P_xyIs the system pressure.

The ion conductivity calculation formula is as follows:

wherein D is ion conductivity, t is time, D is diffusion dimension (three-dimensional diffusion value is 3), N is number of lithium ions, r_iIs the displacement of the ith lithium ion.

Based on the calculation results, the influence of the lithium salt concentration on the dielectric constant, viscosity and ion conductivity of the LiTFSI DOL/DME electrolyte can be analyzed.

As an example of using a finite element method to simulate and calculate physicochemical parameters of an electrolyte, for a DOL/DME electrolyte used for lithium bistrifluoromethanesulfonimide (LiTFSI) of a lithium metal battery, firstly, using steps similar to those of the above example of using molecular dynamics simulation to calculate the dielectric constant, viscosity and ion conductivity of the electrolyte, the physicochemical parameters of the dielectric constant, viscosity and ion conductivity of the DOL/DME electrolyte of the LiTFSI of 0.1-4.0mo/L (every 0.1 mol/L) can be calculated; after the physical and chemical parameters of the DOL/DME electrolyte of the LiTFSI are taken as input parameters and input into a finite element calculation model, COMSOL software can be adopted to carry out finite element simulation on the LiTFSI DOL/DME electrolyte model, and the lithium ion concentration distribution, the electric field distribution and the metal lithium cathode deposition or extraction morphology in the electrolyte under the charging or discharging multiplying power of 0.1, 0.5, 1.0, 2.0, 5.0 and 10.0C are calculated. Furthermore, according to the shapes of the charged and discharged lithium metal negative electrodes, the coulomb efficiency in the battery cycle process can be calculated. Based on the calculation result, the influence of the lithium salt concentration and the charge-discharge rate on the lithium ion concentration distribution, the electric field distribution, the metal lithium negative electrode morphology and the coulomb efficiency of the LiTFSIDOL/DME electrolyte can be analyzed.

As an example of automatically calculating the physicochemical parameters of the electrolyte by adopting various high-throughput calculation methods, for fluoroethylene carbonate (FEC) molecules used for lithium battery electrolytes, the physicochemical parameters of the FEC molecules in a vacuum environment can be calculated by a density functional theory; then, taking the physicochemical parameters of the FEC molecules in the vacuum environment as the input of the molecular dynamics simulation, and calculating the physicochemical parameters of the FEC solvent, such as the dielectric constant, the viscosity, and the like, by using the steps similar to the above example of calculating the dielectric constant and the viscosity of the electrolyte solvent by using the molecular dynamics simulation; then, the physicochemical parameters of the FEC molecules in the vacuum environment can be used as the parameters of the solvation model, the physicochemical parameters of the FEC molecules in the solution environment can be calculated by using the similar steps in the above example of calculating the physicochemical parameters of the solvent molecules in the electrolyte in the solution environment by using the density functional theory, and the physicochemical parameters of the Li molecules in the solution environment can be calculated by using the similar steps in the above example of calculating the physicochemical parameters of the ion-solvent structure formed by the electrolyte solvent molecules and the cations in the solution environment by using the density functional theory⁺The physicochemical parameters of the ion-solvent structure of the FEC in solution; the method similar to the method for calculating the physicochemical parameters of the electrolyte by molecular dynamics simulation can also be adopted to calculate the dissolved LiTFSI, LiFSI and LiPF respectively₆Physicochemical parameters of the FEC electrolyte, such as solvation structure, ionic conductivity, viscosity, dielectric constant and the like; the method similar to the method for calculating the physicochemical parameters of the electrolyte by finite element simulation can also be adopted to calculate the dissolved LiTFSI, LiFSI and LiPF respectively₆The FEC electrolyte has physical and chemical parameters such as lithium ion concentration distribution, electric field distribution, metal lithium negative electrode morphology, coulomb efficiency and the like in the charging and discharging processes.

As an example of synchronously calculating the physical and chemical parameters of the electrolyte by adopting various high-throughput calculation methods, the high-throughput calculation of the electrolyte of the lithium battery is taken as an example for explanation. Firstly, extracting small molecule structure models from an existing small molecule database (such as a GDB-11 or GDB-13 database) in batches; then, the physicochemical parameters of each small molecule and the corresponding electrolyte can be synchronously calculated at high flux by adopting the calculation method in the example of calculating the physicochemical parameters of the electrolyte by adopting the density functional theory, the molecular dynamics simulation and the finite element automatic calculation method; and a large lithium battery electrolyte database can be constructed, wherein the large lithium battery electrolyte database comprises physical and chemical parameters of electrolyte solvent molecules and geometric structures (bond length, bond angle and dihedral angle) of corresponding ion-solvent structures, electronic structures (NBO charge distribution), thermodynamic energy, electron affinity energy, dipole moment, Raman spectrum, infrared spectrum, HOMO and LUMO energy levels, oxidation-reduction potential and the like, and physical and chemical parameters of an electrolyte model such as ion conductivity, viscosity, dielectric constant, melting point and the like.

In step S120, the large amount of first electrolyte obtained in step S110 may be calculated by a high-throughput calculation method, so as to obtain a corresponding relationship between a large amount of electrolyte composition information and physicochemical parameters. Therefore, the processing speed of the corresponding relation between the electrolyte component information and the physical and chemical parameters can be increased, the electrolyte design efficiency is improved, and the electrolyte design cost is saved.

Next, referring to step S130, a training sample is created by using the second physicochemical parameter of each first electrolyte and the composition information of each first electrolyte.

In step S130, training samples may be created from the physicochemical parameters obtained in step S120 by the high-throughput computing method and the corresponding component information, and if a large number of comprehensive training samples can be obtained, the component prediction model obtained by training has higher accuracy and wider applicability.

Finally, step S140 is introduced, the component prediction model is trained according to the plurality of training samples until the training stop condition is satisfied, and the trained component prediction model is obtained.

Wherein the component prediction model may be a machine learning model. In an alternative embodiment, the component prediction model may include at least one of a linear regression analysis model, a logistic regression analysis model, a Support Vector Machine (SVM), a nearest neighbor (KNN) algorithm model, a K-means clustering algorithm model, a decision tree model, a naive bayes model, a random forest model, a dimensionality reduction algorithm model, a gradient enhancement algorithm model. It is to be understood that the component prediction model may be any one of the above models, or may be a combination of two or more of the above models. As an example, for the above-listed models, the prediction effects of different models may be compared, so as to select a suitable model, and thus to realize accurate prediction of the electrolyte property, for example, the prediction effects of each model on the electrolyte composition information under a specific application scenario (high temperature, low temperature, fast charge, high specific energy, etc.) may be compared, or the prediction effects of each model on the electrolyte composition information corresponding to a specific physicochemical parameter or parameters may be compared, so as to select a suitable model.

The training stop condition may be a condition for stopping the training of the component prediction model set in advance. As an example, the training stop condition may be that the loss function of the component prediction model is smaller than a certain threshold, or that the number of iterations of the component prediction model training reaches a certain number. The specific training stopping condition can be selected according to the user requirement, and is not limited herein.

In an optional implementation manner, step S140 may specifically include:

for each training sample, the following steps are respectively carried out:

In the embodiment of the application, a plurality of electrolytes with different component information are simulated through a simulation model, so that a large amount of economic cost and time cost can be saved. The electrolyte simulated by the simulation model is calculated by a high-throughput calculation method, and the physical and chemical parameters corresponding to the electrolyte can be obtained efficiently and at low cost, so that a training sample is created. The training samples created through the process are applied to the training of the component prediction model, and the obtained component prediction model can be processed according to physical and chemical parameters corresponding to the actually required electrolyte performance based on the internal relation between the electrolyte component information and the performance, so that the efficient and low-cost design of the electrolyte is realized.

The electrolyte design method provided by the embodiment of the present application is described in detail below with reference to fig. 2.

Fig. 2 shows a schematic flow chart of an electrolyte design method provided in an embodiment of the present application, and as shown in fig. 2, the data processing method provided in the embodiment of the present application may include steps S210 to S230.

S210, acquiring a first physicochemical parameter of the electrolyte to be designed.

The first physicochemical parameter and the second physicochemical parameter have the same meaning, except that the first physicochemical parameter is a physicochemical parameter corresponding to the electrolyte performance required in practical application, and the second physicochemical parameter corresponds to the physicochemical parameter corresponding to the first electrolyte.

And S220, inputting the first physical and chemical parameters into the trained component prediction model, and predicting the component information of the electrolyte to be designed through the component prediction model to obtain the component information of the electrolyte to be designed, wherein the component prediction model is obtained through training of a training sample obtained based on a high-throughput calculation method.

The component prediction model is obtained by training a plurality of training samples obtained based on a high-throughput computing method, and each training sample can comprise a second physicochemical parameter of a first electrolyte and component information corresponding to the first electrolyte.

And S230, designing the electrolyte to be designed according to the component information of the electrolyte to be designed.

In step S230, one or more formulas of the electrolyte to be designed may be determined according to the component information obtained in step S220, and the electrolyte is configured for experimental verification, or the configuration and verification process of the electrolyte may be simulated by a computer.

According to the electrolyte design method, the component information corresponding to the physical and chemical parameters can be obtained by inputting the physical and chemical parameters of the electrolyte into the component prediction model, so that the components of the electrolyte can be designed according to the performance requirements of the electrolyte without actual experimental work, and the electrolyte meeting the performance requirements can be obtained. In addition, because the training sample of the component prediction model is obtained based on a high-throughput calculation method, a large amount of experiments are not needed to obtain the training sample, and the experiment cost is reduced. The embodiment of the application can realize the design of the electrolyte with low cost and high efficiency.

In an alternative embodiment, the first physicochemical parameter may include at least one of a physicochemical parameter of solvent molecules constituting the electrolyte and a physicochemical parameter of the electrolyte.

Specifically, the physicochemical parameters of solvent molecules in the electrolyte to be designed and/or the physicochemical parameters of the electrolyte as a whole can be determined according to the performance requirements of the electrolyte to be designed, so as to determine the first physicochemical parameters.

In an alternative embodiment, the physical and chemical parameters of the solvent molecules constituting the electrolyte may include at least one of a geometry, an electronic structure, thermodynamic energy, a molecular front orbital level, a dipole moment, an electron affinity, an oxidation-reduction potential, a raman spectrum, an infrared spectrum, a dielectric constant, a viscosity, a melting point, and a boiling point of the solvent molecules.

The physicochemical parameters of the solvent molecules forming the electrolyte comprise at least one of the above physicochemical parameters, so that the designed electrolyte can better meet the expectations of users in terms of performance, thereby avoiding unnecessary repeated labor and saving experimental cost.

In an alternative embodiment, the physical and chemical parameters of the electrolyte may include at least one of a dielectric constant, viscosity, melting point, boiling point, and ion conductivity of the electrolyte.

The physical and chemical parameters of the electrolyte comprise at least one of the physical and chemical parameters, so that the designed electrolyte can better meet the expectations of users in terms of performance, thereby avoiding unnecessary repeated labor and saving experimental cost.

In an alternative embodiment, the compositional information may include the composition of matter and the ratio. Therefore, the formula of the electrolyte to be designed can be directly obtained according to the component information of the electrolyte to be designed, and the experiment cost is further reduced.

In an alternative embodiment, the component prediction model may include at least one of a linear regression analysis model, a logistic regression analysis model, a support vector machine, a nearest neighbor algorithm model, a K-means clustering algorithm model, a decision tree model, a naive bayes model, a random forest model, a dimension reduction algorithm model, and a gradient enhancement algorithm model.

In an alternative embodiment, the electrolyte to be designed may include an aqueous electrolyte and a non-aqueous electrolyte.

In an alternative embodiment, the electrolyte to be designed may include a solvent, an electrolyte salt, and optionally additives.

Wherein, the solvent can comprise one or more organic small molecules, and the organic small molecules can be composed of at least two elements of C, H, O, N, F, Cl, S, P and B;

the electrolyte salt may include at least one of hexafluorophosphate, bisfluorosulfonylimide, nitrate, nitrite, fluoride, chloride, bromide, iodide, difluorophosphate, difluorooxalato borate, dioxaoxalato borate, tetrafluorooxalato borate, bisfluorosulfonylimide, bistrifluoromethylsulfonylimide, 4, 5-dicyano-2-trifluoromethylimidazolium, perchlorate, sulfate, sulfite, hexafluoroarsenate, and a cation in the electrolyte salt may be selected from Li⁺、Na⁺、K⁺、Mg²⁺、Ca²⁺、Zn²⁺、Al³⁺At least one of (a);

As an example, the optional additives may include one or more organic small molecules composed of carbon, hydrogen, oxygen, nitrogen, fluorine, chlorine, sulfur, phosphorus, boron elements, and one or more of salts such as hexafluorophosphate, bis-fluorosulfonylimide, nitrate, nitrite, fluoride, chloride, bromide, iodide, difluorophosphate, difluorooxalato borate, dioxaoxalato borate, bis-fluorosulfonylimide, bis-trifluoromethanesulfonimide, 4, 5-dicyano-2-trifluoromethylimidazole, perchlorate, sulfate, sulfite, hexafluoroarsenate, and the like. Wherein the cation of the salt is selected from Li⁺、Na⁺、K⁺、Mg²⁺、Ca²⁺、Zn²⁺、Al³⁺At least one of (a).

Based on the same inventive concept, the embodiment of the present application further provides an electrolyte solution designing apparatus 300.

As shown in fig. 3, the electrolyte design apparatus 300 may include an acquisition module 301, an input module 302, and a design module 303.

The obtaining module 301 is configured to obtain a first physicochemical parameter of an electrolyte to be designed.

The prediction module 302 is configured to input the first physicochemical parameter into the trained component prediction model, and predict component information of the electrolyte to be designed through the component prediction model to obtain the component information of the electrolyte to be designed, where the component prediction model is obtained through training of a training sample obtained based on a high-throughput computing method.

The design module 303 is configured to design the electrolyte to be designed according to the component information of the electrolyte to be designed.

In an alternative embodiment, the apparatus 300 may further include a simulation module, a calculation module, a creation module, and a training module.

And the simulation module is used for simulating a plurality of first electrolytes with different composition information through a pre-trained simulation model.

And the calculation module is used for calculating the first electrolyte by using a high-throughput calculation method to obtain a second physicochemical parameter corresponding to the first electrolyte.

And the creating module is used for creating the training sample by using the second physicochemical parameter of each first electrolyte and the composition information of each first electrolyte.

And the training module is used for training the component prediction model according to the plurality of training samples until the training stopping condition is met, so as to obtain the trained component prediction model.

In an optional implementation manner, the training module is configured to train the component prediction model according to a plurality of training samples until a training stop condition is satisfied, so as to obtain a trained component prediction model, and specifically, the training module may include:

for each training sample, the following steps are respectively carried out:

In an alternative embodiment, the high-throughput computing method may include at least one of density functional theory calculations, molecular dynamics simulations, and finite element calculations.

In an alternative embodiment, the first physicochemical parameter includes at least one of a physicochemical parameter of solvent molecules constituting the electrolyte and a physicochemical parameter of the electrolyte.

In an alternative embodiment, the compositional information may include the composition of matter and the ratio.

In an alternative embodiment, the electrolyte to be designed may include a solvent, an electrolyte salt, and optionally additives,

Fig. 4 shows a hardware structure schematic diagram of an electrolyte design device provided in an embodiment of the present application.

The apparatus in the electrolyte design may include a processor 401 and a memory 402 storing computer program instructions.

Specifically, the processor 401 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 402 may include mass storage for data or instructions. By way of example, and not limitation, memory 402 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, tape, or Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 402 may include removable or non-removable (or fixed) media, where appropriate. The memory 402 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 402 is a non-volatile solid-state memory.

The memory may include Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to the methods according to an aspect of the application.

The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the electrolyte design methods in the above embodiments.

In one example, the electrolyte design apparatus may also include a communication interface 403 and a bus 410. As shown in fig. 4, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 to complete communication therebetween.

The communication interface 403 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application.

Bus 410 comprises hardware, software, or both that couple the components of the online data traffic billing device to one another. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 410 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

The electrolyte design device can execute the method for designing the electrolyte in the embodiment of the application based on the first physicochemical parameter of the electrolyte to be designed, so as to realize the electrolyte design method and the device described in connection with fig. 2 and 3.

In addition, in combination with the electrolyte design method in the above embodiments, the embodiments of the present application can be implemented by providing a computer storage medium. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any of the electrolyte design methods in the above embodiments.

It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.

The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.

Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.

As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.

Claims

1. An electrolyte design method, comprising:

inputting the first physical and chemical parameters into a trained component prediction model, predicting component information of the electrolyte to be designed through the component prediction model, and obtaining the component information of the electrolyte to be designed, wherein the component prediction model is obtained through training of a training sample obtained based on a high-throughput computing method;

2. The method according to claim 1, wherein before the inputting the first physicochemical parameter into a trained component prediction model and predicting the component information of the electrolyte to be designed through the component prediction model to obtain the component information of the electrolyte to be designed, the method further comprises:

respectively creating a training sample by using the second physicochemical parameter of each first electrolyte and the composition information of each first electrolyte;

and training the component prediction model according to the plurality of training samples until a training stopping condition is met, and obtaining the trained component prediction model.

3. The method of claim 2, wherein training the component prediction model based on the plurality of training samples until a training stop condition is met to obtain the trained component prediction model comprises:

for each training sample, respectively executing the following steps:

inputting the training samples into a preset component prediction model to obtain predicted component information corresponding to the second physicochemical parameter;

4. The method of claim 2, wherein the high-throughput computing method comprises at least one of density functional theory calculations, molecular dynamics simulations, and finite element calculations.

5. The method according to claim 1, characterized in that said first physicochemical parameter comprises at least one of a physicochemical parameter of solvent molecules constituting said electrolyte and a physicochemical parameter of said electrolyte.

6. The method according to claim 5, wherein the physicochemical parameters of the solvent molecules constituting the electrolyte comprise at least one of a geometry, an electronic structure, thermodynamic energy, a molecular front orbital level, a dipole moment, an electron affinity, an oxidation-reduction potential, a Raman spectrum, an infrared spectrum, a dielectric constant, a viscosity, a melting point, and a boiling point of the solvent molecules.

7. The method of claim 5, wherein the physical and chemical parameters of the electrolyte comprise at least one of a dielectric constant, a viscosity, a melting point, a boiling point, and an ionic conductivity of the electrolyte.

8. The method of claim 1, wherein the compositional information includes material composition and ratio.

9. The method of claim 1, wherein the component prediction model comprises at least one of a linear regression analysis model, a logistic regression analysis model, a support vector machine, a nearest neighbor algorithm model, a K-means clustering algorithm model, a decision tree model, a naive bayes model, a random forest model, a dimension reduction algorithm model, a gradient enhancement algorithm model.

10. The method according to any one of claims 1 to 9, wherein the electrolyte to be designed comprises an aqueous electrolyte and a non-aqueous electrolyte.

11. The method according to any of claims 1 to 9, characterized in that the electrolyte to be designed comprises a solvent, an electrolyte salt and optionally additives,

the electrolyte salt includes hexafluorophosphate, bifluorosulfonyl imide salt, nitrate, nitrite, fluoride salt, chloride salt, bromide salt, iodide salt, and difluorophosphate saltAt least one of difluorooxalato borate, dioxaoxalato borate, tetrafluorooxalato borate, bisfluorosulfonyl imide, bistrifluoromethylsulfonyl imide, 4, 5-dicyano-2-trifluoromethylimidazolium, perchlorate, sulfate, sulfite, hexafluoroarsenate, and a cation in the electrolyte salt is selected from Li⁺、Na⁺、K⁺、Mg²⁺、Ca²⁺、Zn²⁺、Al³⁺At least one of (a);

12. An electrolyte design apparatus, comprising:

the prediction module is used for inputting the first physical and chemical parameters into a trained component prediction model, predicting component information of the electrolyte to be designed through the component prediction model, and obtaining the component information of the electrolyte to be designed, wherein the component prediction model is obtained through training of a training sample obtained based on a high-throughput computing method;

13. An electrolyte design apparatus, comprising: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements the electrolyte design method of any of claims 1-11.

14. A computer readable storage medium having computer program instructions stored thereon which, when executed by a processor, implement the electrolyte design method of any one of claims 1 to 11.

15. A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the electrolyte design method of any of claims 1-11.