Processing method and device for predicting physical properties of electrolyte formula components

Abstract

The embodiment of the invention relates to a processing method and device for predicting the physical properties of electrolyte formula components. The method comprises the following steps: constructing a molecular physical property prediction model; performing model training on the molecular physical property prediction model based on a preset first data set; after model training is finished, any electrolyte formula input by a user is received and recorded as a corresponding first formula; and performing electrolyte formula component physical property prediction processing on the first formula by using the molecular physical property prediction model to obtain a corresponding first physical property analysis report which is fed back to a user. The electrolyte formula is subjected to component physical property analysis, so that the analysis complexity can be reduced, the analysis period can be shortened, and the analysis efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a processing method and device for predicting the physical properties of electrolyte formula components. Background Art

[0002] The electrolyte formula consists of three components: solvent, electrolyte and additive, and each component corresponds to a molecular structure. When designing and developing the electrolyte formula, it is necessary to analyze the various physical properties corresponding to the formula components (such as melting point, boiling point, vapor pressure, dielectric constant, refractive index, density, synthesizability, etc.). At present, there are roughly two types of conventional analysis methods: analysis methods based on simulation calculations and analysis methods based on experiments. Among them, the analysis methods based on simulation calculations are roughly divided into the following steps: first, three-dimensional modeling is performed based on the molecular information of each formula component (such as one-dimensional molecular sequence, two-dimensional molecular topological structure) through chemical informatics tools (such as OpenBabel, RDKit, etc.), and then based on molecular dynamics (Molecular Dynamic, MD) simulation tools (such as LAMMPS, GROMACS, NAMD, etc.), molecular motion simulation is performed according to the modeling structure to obtain the corresponding simulation structure, based on the trajectory composed of multiple simulation structures combined with the energy, force, dipole moment, etc. corresponding to each structure generated in the simulation, or based on quantum chemical calculation tools (such as Gaussian, GAMESS, etc.) to calculate the properties according to the simulation structure. The experimental analysis method is roughly divided into the following steps: first, prepare the ingredients based on the molecular information of the formula ingredients (such as one-dimensional molecular sequence), and perform synthesizability analysis based on the effect of the ingredient preparation, and when the synthesizability analysis results meet the standards, measure other physical properties of the formula ingredients (such as melting point, boiling point, vapor pressure, dielectric constant, refractive index, density, etc.) based on a series of experimental measurement methods. In practice, we found that both of the above analysis methods have defects such as complex analysis operations, long analysis cycles, and low analysis efficiency. Summary of the invention

[0003] The purpose of the present invention is to provide a processing method, device, electronic device and computer-readable storage medium for predicting the physical properties of electrolyte formula components in view of the defects of the prior art. The present invention first constructs a molecular property prediction model based on a Uni-Mol model and seven nonlinear regression models, and then enables the model to be trained to predict seven types of physical properties (melting point, boiling point, vapor pressure, dielectric constant, refractive index, density and synthesizability) of one / two / three-dimensional molecular structures and output the corresponding physical property prediction sequence; after the model training is completed, the molecular physical property prediction model is used to analyze the seven types of physical properties of all formula components of any electrolyte formula input by the user and the obtained physical property analysis report is fed back to the user. By performing component physical property analysis on the electrolyte formula by the present invention, the analysis complexity can be reduced, the analysis cycle can be shortened, and the analysis efficiency can be improved.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present invention provides a method for predicting the physical properties of electrolyte formula components, the method comprising:

[0005] Constructing a molecular property prediction model; the molecular property prediction model is used to predict seven types of physical properties according to the input formula component information and output a corresponding property prediction sequence; wherein the formula component information includes molecular structure type and molecular structure information; the molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the molecular structure type is one-dimensional, the corresponding molecular structure information is a one-dimensional molecular sequence, when the molecular structure type is two-dimensional, the corresponding molecular structure information is a two-dimensional molecular topology map, and when the molecular structure type is three-dimensional, the corresponding molecular structure information is a three-dimensional molecular conformation; the seven types of physical properties include melting point, boiling point, vapor pressure, dielectric constant, refractive index, density and synthesizability; the property prediction sequence includes melting point prediction value, boiling point prediction value, vapor pressure prediction value, dielectric constant prediction value, refractive index prediction value, density prediction value and synthesizability prediction value;

[0006] Model training is performed on the molecular property prediction model based on a preset first data set; wherein the first data set includes a plurality of first data records; the first data record includes a first training molecular structure type, first training molecular structure information and a first physical property label sequence; the first training molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first training molecular structure type is a one-dimensional type, the corresponding first training molecular structure information is a one-dimensional molecular sequence, when the first training molecular structure type is a two-dimensional type, the corresponding first training molecular structure information is a two-dimensional molecular topology map, and when the first training molecular structure type is a three-dimensional type, the corresponding first training molecular structure information is a three-dimensional molecular conformation; the first physical property label sequence includes a first melting point label value, a first boiling point label value, a first vapor pressure label value, a first dielectric constant label value, a first refractive index label value, a first density label value and a first synthesizability label value;

[0007] After the model training is completed, any electrolyte formula input by the user is received and recorded as the corresponding first formula; and the molecular physical property prediction model is used to predict the physical properties of the electrolyte formula components of the first formula to obtain a corresponding first physical property analysis report to be fed back to the user; wherein, the first formula includes multiple first formula component information; the first formula component information includes a first molecular structure type and a first molecular structure information; the first molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first molecular structure type is a one-dimensional type, the corresponding first molecular structure information is a one-dimensional molecular sequence, when the first molecular structure type is a two-dimensional type, the corresponding first molecular structure information is a two-dimensional molecular topology map, and when the first molecular structure type is a three-dimensional type, the corresponding first molecular structure information is a three-dimensional molecular conformation; the first physical property analysis report includes multiple first component analysis records; the first component analysis record includes the first formula component information and the corresponding first physical property analysis sequence.

[0008] Preferably, the model input end of the molecular physical property prediction model is used to receive the input formula component information, and the model output end is used to output the corresponding physical property prediction sequence;

[0009] The molecular property prediction model includes a molecular structure initialization module, a molecular structure optimization module, a melting point prediction module, a boiling point prediction module, a vapor pressure prediction module, a dielectric constant prediction module, a refractive index prediction module, a density prediction module, a synthesizability prediction module and a molecular property output module;

[0010] The input end of the molecular structure initialization module is connected to the input end of the model, and the output end is connected to the input end of the molecular structure optimization module; the output end of the molecular structure optimization module is respectively connected to the input ends of the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module; the seven input ends of the molecular property output module are respectively connected to the output ends of the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module, and the output section of the molecular property output module is connected to the model output end;

[0011] The molecular structure initialization module is used to extract the corresponding molecular structure type and the molecular structure information from the formula ingredient information as the corresponding current type and current structure information; and identify the current type; if the current type is a one-dimensional type, the current structure information is used as the corresponding first molecular sequence, and a one-dimensional sequence processing interface provided by a preset chemical informatics tool is used to perform three-dimensional molecular structure modeling processing based on the first molecular sequence to obtain the corresponding initialized molecular conformation; if the current type is a two-dimensional type, the current structure information is used as the corresponding first molecular topology map, and a two-dimensional topology map processing interface provided by the chemical informatics tool is used to perform three-dimensional molecular structure modeling processing based on the first molecular topology map to obtain the corresponding initialized molecular conformation; if the current type is a two-dimensional type If the type is a three-dimensional type, the current structure information is used as the corresponding initialized molecular conformation; and the obtained initialized molecular conformation is sent to the molecular structure optimization module; wherein the chemical informatics tool includes at least OpenBabel and RDKit; the one-dimensional sequence processing interface is a processing interface provided by the chemical informatics tool, and the one-dimensional sequence processing interface is used to perform a corresponding three-dimensional molecular conformation conversion according to the one-dimensional molecular sequence input by the interface and output the obtained three-dimensional molecular conformation as interface output data; the two-dimensional topology map processing interface is a processing interface provided by the chemical informatics tool, and the two-dimensional topology map processing interface is used to perform a corresponding three-dimensional molecular conformation conversion according to the two-dimensional molecular topology map input by the interface and output the obtained three-dimensional molecular conformation as interface output data;

[0012] The molecular structure optimization module is implemented based on a Uni-Mol model that has completed model pre-training; the molecular structure optimization module is used to perform three-dimensional structural optimization processing on the initialized molecular conformation to obtain a corresponding optimized molecular conformation; and the optimized molecular conformation is sent to the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module respectively;

[0013] The melting point prediction module is implemented based on a first nonlinear regression model; the melting point prediction module is used to perform melting point prediction processing according to the optimized molecular conformation to obtain the corresponding melting point prediction value and send it to the molecular property output module; the first nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model;

[0014] The boiling point prediction module is implemented based on a second nonlinear regression model; the boiling point prediction module is used to perform boiling point prediction processing according to the optimized molecular conformation to obtain the corresponding boiling point prediction value and send it to the molecular property output module; the second nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model;

[0015] The vapor pressure prediction module is implemented based on a third nonlinear regression model; the vapor pressure prediction module is used to perform vapor pressure prediction processing according to the optimized molecular conformation to obtain the corresponding vapor pressure prediction value and send it to the molecular property output module; the third nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model;

[0016] The dielectric constant prediction module is implemented based on a fourth nonlinear regression model; the dielectric constant prediction module is used to perform dielectric constant prediction processing according to the optimized molecular conformation to obtain the corresponding dielectric constant prediction value and send it to the molecular property output module; the fourth nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model;

[0017] The refractive index prediction module is implemented based on the fifth nonlinear regression model; the refractive index prediction module is used to perform refractive index prediction processing according to the optimized molecular conformation to obtain the corresponding refractive index prediction value and send it to the molecular property output module; the fifth nonlinear regression model at least includes XGBoost model, GBDT model, Random Forest model and MLP model;

[0018] The density prediction module is implemented based on the sixth nonlinear regression model; the density prediction module is used to perform density prediction processing according to the optimized molecular conformation to obtain the corresponding density prediction value and send it to the molecular property output module; the sixth nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model;

[0019] The synthesizability prediction module is implemented based on the seventh nonlinear regression model; the synthesizability prediction module is used to perform synthesizability prediction processing according to the optimized molecular conformation to obtain the corresponding synthesizability prediction value and send it to the molecular property output module; the seventh nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model;

[0020] The molecular property output module is used to compose and output the corresponding property prediction sequence from the obtained melting point prediction value, boiling point prediction value, vapor pressure prediction value, dielectric constant prediction value, refractive index prediction value, density prediction value and synthesizability prediction value.

[0021] Preferably, the model training of the molecular property prediction model based on the preset first data set specifically includes:

[0022] Step 31, randomly splitting the first data set based on a preset first test evaluation ratio to obtain a corresponding first test set and a first evaluation set;

[0023] Wherein, both the first test set and the first evaluation set are composed of a plurality of the first data records; the ratio of the total number of records in the first test set to the total number of records in the first evaluation set satisfies the first test evaluation ratio;

[0024] Step 32, setting the corresponding first debugging object as a nonlinear regression model; and extracting the first first data record of the first test set as the corresponding current test record;

[0025] Wherein, the first debugging object includes a nonlinear regression model and a structural optimization model;

[0026] Step 33, forming a corresponding first training formula ingredient information from the first training molecular structure type and the first training molecular structure information of the current test record;

[0027] Step 34, inputting the first training formula ingredient information into the molecular physical property prediction model to perform seven types of physical property predictions and obtain a corresponding first physical property prediction sequence;

[0028] Wherein, the first physical property prediction sequence includes a first melting point prediction value, a first boiling point prediction value, a first vapor pressure prediction value, a first dielectric constant prediction value, a first refractive index prediction value, a first density prediction value and a first synthesizability prediction value;

[0029] Step 35, bringing the first physical property prediction sequence and the first physical property label sequence of the current test record into a preset first model loss function to calculate and obtain a corresponding first loss value;

[0030] Wherein, the first model loss function at least includes an L1 loss function, an L2 loss function and a cross entropy loss function;

[0031] Step 36, identifying whether the first loss value satisfies a preset first loss value range; if the first loss value satisfies the first loss value range, identifying whether the current test record is the last first data record of the first test set, if so, go to step 37, otherwise extract the next first data record of the first test set as the new current test record and return to step 33; if the first loss value does not satisfy the first loss value range, identifying the first debugging object, if the first debugging object is a nonlinear regression model, under the premise that the model parameters of the molecular structure optimization module remain unchanged, the melting point prediction module, based on the preset first model parameter optimizer, is optimized in the direction of minimizing the first model loss function. The model parameters of the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module are subjected to a round of model parameter modulation and the step 34 is returned when the current round of parameter modulation is completed. If the first debugging object is a structure optimization model, the model parameters of the molecular structure optimization module are subjected to a round of model parameter fine-tuning based on a preset second model parameter optimizer in the direction of minimizing the first model loss function under the premise that the model parameters of the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module remain unchanged, and the step 34 is returned when the current round of parameter fine-tuning is completed.

[0032] Wherein, the first model parameter optimizer includes at least an SGD optimizer and an ADAM optimizer; the second model parameter optimizer includes at least an RMSprop optimizer, an AdamW optimizer and an ADAM optimizer;

[0033] Step 37, identifying whether the first debugging object is a structural optimization model; if so, going to step 38; if not, setting the first debugging object as a structural optimization model, extracting the first first data record of the first test set as the new current test record, and returning to step 33;

[0034] Step 38, traverse all the first data records of the first evaluation set; and in the traversal process, use the first data record currently traversed as the corresponding current evaluation record; and form a corresponding second training formula ingredient information from the first training molecular structure type and the first training molecular structure information of the current evaluation record; and input the second training formula ingredient information into the molecular property prediction model to perform seven types of physical property predictions and obtain a corresponding second property prediction sequence; and form a corresponding prediction-label pair from the second property prediction sequence and the first property label sequence of the current test record; and at the end of the traversal, bring all the obtained prediction-label pairs into the preset first model evaluation function to calculate and obtain the corresponding first evaluation value;

[0035] Wherein, the first model evaluation function at least includes an RMSE function;

[0036] Step 39, identifying whether the first evaluation value meets the preset first evaluation value range; if not, returning to step 31 to continue training; if satisfied, stopping training and confirming the end of model training.

[0037] Preferably, the using the molecular physical property prediction model to perform physical property prediction processing on the first formula for electrolyte formula components to obtain a corresponding first physical property analysis report and feed it back to the user specifically includes:

[0038] Input the first formula component information of each of the first formula into the molecular physical property prediction model to predict seven types of physical properties and use the physical property prediction sequence obtained this time as the corresponding first physical property analysis sequence; and each of the first formula component information and the corresponding first physical property analysis sequence constitute a corresponding first component analysis record; and all the first component analysis records obtained constitute a corresponding first physical property analysis report to be fed back to the user.

[0039] A second aspect of an embodiment of the present invention provides a device for implementing the processing method for predicting the physical properties of electrolyte formula components described in the first aspect, the device comprising: a model building module, a model training module and a model application module;

[0040] The model building module is used to build a molecular property prediction model; the molecular property prediction model is used to predict seven types of physical properties according to the input formula component information and output the corresponding property prediction sequence; wherein the formula component information includes molecular structure type and molecular structure information; the molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the molecular structure type is one-dimensional, the corresponding molecular structure information is a one-dimensional molecular sequence, when the molecular structure type is two-dimensional, the corresponding molecular structure information is a two-dimensional molecular topology map, and when the molecular structure type is three-dimensional, the corresponding molecular structure information is a three-dimensional molecular conformation; the seven types of physical properties include melting point, boiling point, vapor pressure, dielectric constant, refractive index, density and synthesizability; the property prediction sequence includes melting point prediction value, boiling point prediction value, vapor pressure prediction value, dielectric constant prediction value, refractive index prediction value, density prediction value and synthesizability prediction value;

[0041] The model training module is used to perform model training on the molecular property prediction model based on a preset first data set; wherein the first data set includes multiple first data records; the first data record includes a first training molecular structure type, first training molecular structure information and a first physical property label sequence; the first training molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first training molecular structure type is a one-dimensional type, the corresponding first training molecular structure information is a one-dimensional molecular sequence, when the first training molecular structure type is a two-dimensional type, the corresponding first training molecular structure information is a two-dimensional molecular topology map, and when the first training molecular structure type is a three-dimensional type, the corresponding first training molecular structure information is a three-dimensional molecular conformation; the first physical property label sequence includes a first melting point label value, a first boiling point label value, a first vapor pressure label value, a first dielectric constant label value, a first refractive index label value, a first density label value and a first synthesizability label value;

[0042] The model application module is used to receive any electrolyte formula input by the user as the corresponding first formula after the model training is completed; and use the molecular physical property prediction model to predict the physical properties of the electrolyte formula components of the first formula to obtain the corresponding first physical property analysis report and feedback it to the user; wherein, the first formula includes multiple first formula component information; the first formula component information includes a first molecular structure type and a first molecular structure information; the first molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first molecular structure type is a one-dimensional type, the corresponding first molecular structure information is a one-dimensional molecular sequence, when the first molecular structure type is a two-dimensional type, the corresponding first molecular structure information is a two-dimensional molecular topology map, and when the first molecular structure type is a three-dimensional type, the corresponding first molecular structure information is a three-dimensional molecular conformation; the first physical property analysis report includes multiple first component analysis records; the first component analysis record includes the first formula component information and the corresponding first physical property analysis sequence.

[0043] A third aspect of an embodiment of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;

[0044] The processor is used to be coupled to the memory, read and execute instructions in the memory, so as to implement the method steps described in the first aspect above;

[0045] The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.

[0046] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are executed by a computer, the computer executes the instructions of the method described in the first aspect above.

[0047] The embodiment of the present invention provides a processing method, device, electronic device and computer-readable storage medium for predicting the physical properties of electrolyte formula components. As can be seen from the above content, the embodiment of the present invention first constructs a molecular property prediction model based on a Uni-Mol model and seven nonlinear regression models, and then through model training, it enables it to predict seven types of physical properties (melting point, boiling point, vapor pressure, dielectric constant, refractive index, density and synthesizability) of one / two / three-dimensional molecular structures and output the corresponding physical property prediction sequence; after the model training is completed, the molecular physical property prediction model is used to analyze the seven types of physical properties of all formula components of any electrolyte formula input by the user and the obtained physical property analysis report is fed back to the user. By performing component physical property analysis on the electrolyte formula through the embodiment of the present invention, the analysis complexity is reduced, the analysis cycle is shortened, and the analysis efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of a processing method for predicting the physical properties of electrolyte formula components provided in Example 1 of the present invention;

[0049] Figure 2 A schematic diagram of a module of a molecular property prediction model provided in Example 1 of the present invention;

[0050] Figure 3 A module structure diagram of a processing device for predicting the physical properties of electrolyte formula components provided in the second embodiment of the present invention;

[0051] Figure 4 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Embodiment 1 of the present invention provides a method for predicting the physical properties of electrolyte formulation components, such as Figure 1 As shown in the schematic diagram of a processing method for predicting the physical properties of electrolyte formula components provided in the first embodiment of the present invention, the method mainly includes the following steps:

[0054] Step 1: Construct a molecular property prediction model.

[0055] Here, the molecular property prediction model of an embodiment of the present invention is used to predict seven types of physical properties based on the input formula ingredient information and output the corresponding property prediction sequence; wherein the formula ingredient information includes molecular structure type and molecular structure information; the molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the molecular structure type is one-dimensional, the corresponding molecular structure information is a one-dimensional molecular sequence (such as a SMILES sequence), when the molecular structure type is two-dimensional, the corresponding molecular structure information is a two-dimensional molecular topology map, and when the molecular structure type is three-dimensional, the corresponding molecular structure information is a three-dimensional molecular conformation; the seven types of physical properties include melting point, boiling point, vapor pressure, dielectric constant, refractive index, density and synthesizability; the property prediction sequence includes melting point prediction value, boiling point prediction value, vapor pressure prediction value, dielectric constant prediction value, refractive index prediction value, density prediction value and synthesizability prediction value.

[0056] like Figure 2 As shown in the module schematic diagram of the molecular property prediction model provided in the first embodiment of the present invention, the model input end of the molecular property prediction model of the embodiment of the present invention is used to receive input formula component information, and the model output end is used to output a corresponding property prediction sequence; and the molecular property prediction model includes a molecular structure initialization module, a molecular structure optimization module, a melting point prediction module, a boiling point prediction module, a vapor pressure prediction module, a dielectric constant prediction module, a refractive index prediction module, a density prediction module, a synthesizability prediction module and a molecular property output module;

[0057] The connection relationship of each module of the molecular property prediction model is as follows: the input end of the molecular structure initialization module is connected to the model input end, and the output end is connected to the input end of the molecular structure optimization module; the output end of the molecular structure optimization module is respectively connected to the input ends of the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module; the seven input ends of the molecular property output module are respectively connected to the output ends of the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module, and the output segment of the molecular property output module is connected to the model output end;

[0058] It should be noted that the molecular structure initialization module of the molecular property prediction model of the embodiment of the present invention is used to extract the corresponding molecular structure type and molecular structure information from the formula ingredient information as the corresponding current type and current structure information; and identify the current type; if the current type is a one-dimensional type, the current structure information is used as the corresponding first molecular sequence, and the one-dimensional sequence processing interface provided by the preset chemical informatics tool is used to perform three-dimensional molecular structure modeling processing according to the first molecular sequence to obtain the corresponding initialized molecular conformation; if the current type is a two-dimensional type, the current structure information is used as the corresponding first molecular topology map, and the two-dimensional topology map processing interface provided by the chemical informatics tool is used to perform three-dimensional molecular structure modeling processing according to the first molecular topology map to obtain the corresponding initialized molecular conformation; if the current type is a two-dimensional type, the current structure information is used as the corresponding first molecular topology map, and the two-dimensional topology map processing interface provided by the chemical informatics tool is used to perform three-dimensional molecular structure modeling processing according to the first molecular topology map to obtain the corresponding initialized molecular conformation; If the type is a three-dimensional type, the current structure information is used as the corresponding initialized molecular conformation; and the obtained initialized molecular conformation is sent to the molecular structure optimization module; wherein the chemical informatics tool of the embodiment of the present invention includes at least OpenBabel and RDKit; the above-mentioned one-dimensional sequence processing interface is a processing interface provided by a chemical informatics tool, and the one-dimensional sequence processing interface is used to perform a corresponding three-dimensional molecular conformation conversion according to the one-dimensional molecular sequence input by the interface and output the obtained three-dimensional molecular conformation as interface output data; the above-mentioned two-dimensional topology map processing interface is a processing interface provided by a chemical informatics tool, and the two-dimensional topology map processing interface is used to perform a corresponding three-dimensional molecular conformation conversion according to the two-dimensional molecular topology map input by the interface and output the obtained three-dimensional molecular conformation as interface output data;

[0059] It should be noted that the molecular structure optimization module of the molecular property prediction model of the embodiment of the present invention is implemented based on a Uni-Mol model that has completed model pre-training; the molecular structure optimization module is used to perform three-dimensional structural optimization processing on the initialized molecular conformation to obtain the corresponding optimized molecular conformation; and the optimized molecular conformation is sent to the melting point prediction module, boiling point prediction module, vapor pressure prediction module, dielectric constant prediction module, refractive index prediction module, density prediction module and synthesizability prediction module respectively; the Uni-Mol model mentioned here can be understood by referring to the paper "UNI-MOL: A UNIVERSAL 3DMOLECULAR REPRESENTATION LEARNING FRAMEWORK", which explains the pre-training method of the model, and the pre-training of the Uni-Mol model can be completed by referring to the content of the paper. The paper also points out that the Uni-Mol model can perform structural optimization on three-dimensional molecular conformations. Therefore, the embodiment of the present invention implements the module function of the molecular structure optimization module based on a Uni-Mol model that has completed model pre-training;

[0060] It should be noted that the melting point prediction module of the molecular property prediction model of the embodiment of the present invention is implemented based on the first nonlinear regression model; the melting point prediction module is used to perform melting point prediction processing according to the optimized molecular conformation to obtain the corresponding melting point prediction value and send it to the molecular property output module; here, the optional model types of the first nonlinear regression model of the embodiment of the present invention include at least XGBoost model, GBDT model, Random Forest model and MLP model;

[0061] It should be noted that the boiling point prediction module of the molecular property prediction model of the embodiment of the present invention is implemented based on the second nonlinear regression model; the boiling point prediction module is used to perform boiling point prediction processing according to the optimized molecular conformation to obtain the corresponding boiling point prediction value and send it to the molecular property output module; here, the optional model types of the second nonlinear regression model of the embodiment of the present invention include at least XGBoost model, GBDT model, Random Forest model and MLP model;

[0062] It should be noted that the vapor pressure prediction module of the molecular property prediction model of the embodiment of the present invention is implemented based on the third nonlinear regression model; the vapor pressure prediction module is used to perform vapor pressure prediction processing according to the optimized molecular conformation to obtain the corresponding vapor pressure prediction value and send it to the molecular property output module; here, the optional model types of the third nonlinear regression model of the embodiment of the present invention include at least XGBoost model, GBDT model, Random Forest model and MLP model;

[0063] It should be noted that the dielectric constant prediction module of the molecular property prediction model of the embodiment of the present invention is implemented based on the fourth nonlinear regression model; the dielectric constant prediction module is used to perform dielectric constant prediction processing according to the optimized molecular conformation to obtain the corresponding dielectric constant prediction value and send it to the molecular property output module; here, the optional model types of the fourth nonlinear regression model of the embodiment of the present invention include at least XGBoost model, GBDT model, Random Forest model and MLP model;

[0064] It should be noted that the refractive index prediction module of the molecular property prediction model of the embodiment of the present invention is implemented based on the fifth nonlinear regression model; the refractive index prediction module is used to perform refractive index prediction processing according to the optimized molecular conformation to obtain the corresponding refractive index prediction value and send it to the molecular property output module; here, the optional model types of the fifth nonlinear regression model of the embodiment of the present invention include at least XGBoost model, GBDT model, Random Forest model and MLP model;

[0065] It should be noted that the density prediction module of the molecular property prediction model of the embodiment of the present invention is implemented based on the sixth nonlinear regression model; the density prediction module is used to perform density prediction processing according to the optimized molecular conformation to obtain the corresponding density prediction value and send it to the molecular property output module; here, the optional model types of the sixth nonlinear regression model of the embodiment of the present invention include at least XGBoost model, GBDT model, Random Forest model and MLP model;

[0066] It should be noted that the synthesizability prediction module of the molecular property prediction model of the embodiment of the present invention is implemented based on the seventh nonlinear regression model; the synthesizability prediction module is used to perform synthesizability prediction processing according to the optimized molecular conformation to obtain the corresponding synthesizability prediction value and send it to the molecular property output module; here, the optional model types of the seventh nonlinear regression model of the embodiment of the present invention include at least XGBoost model, GBDT model, Random Forest model and MLP model;

[0067] It should be noted that the molecular property output module of the molecular property prediction model of the embodiment of the present invention is used to compose a corresponding property prediction sequence from the obtained melting point prediction value, boiling point prediction value, vapor pressure prediction value, dielectric constant prediction value, refractive index prediction value, density prediction value and synthesizability prediction value and output it.

[0068] Step 2, performing model training on the molecular property prediction model based on a preset first data set;

[0069] Among them, the first data set of the embodiment of the present invention is a model training data set constructed in advance through big data collection, and is composed of multiple first data records; the first data record includes a first training molecular structure type, a first training molecular structure information and a first physical property label sequence; the first training molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first training molecular structure type is a one-dimensional type, the corresponding first training molecular structure information is a one-dimensional molecular sequence, when the first training molecular structure type is a two-dimensional type, the corresponding first training molecular structure information is a two-dimensional molecular topology map, and when the first training molecular structure type is a three-dimensional type, the corresponding first training molecular structure information is a three-dimensional molecular conformation; the first physical property label sequence includes a first melting point label value, a first boiling point label value, a first vapor pressure label value, a first dielectric constant label value, a first refractive index label value, a first density label value and a first synthesizability label value;

[0070] Specifically, it includes: step 21, randomly dividing the first data set based on a preset first test evaluation ratio to obtain a corresponding first test set and a first evaluation set;

[0071] The first test evaluation ratio is a preset ratio value, such as 8:2; the first test set and the first evaluation set are both composed of a plurality of first data records; the ratio of the total number of records in the first test set to the total number of records in the first evaluation set satisfies the first test evaluation ratio;

[0072] Step 22, setting the corresponding first debugging object as a nonlinear regression model; and extracting the first first data record of the first test set as the corresponding current test record;

[0073] Wherein, the first debugging object includes a nonlinear regression model and a structural optimization model;

[0074] Step 23, forming a corresponding first training formula ingredient information from the first training molecular structure type and the first training molecular structure information of the current test record;

[0075] Step 24, inputting the first training formula ingredient information into the molecular physical property prediction model to perform seven types of physical property predictions and obtain a corresponding first physical property prediction sequence;

[0076] The first physical property prediction sequence includes a first melting point prediction value, a first boiling point prediction value, a first vapor pressure prediction value, a first dielectric constant prediction value, a first refractive index prediction value, a first density prediction value and a first synthesizability prediction value;

[0077] Step 25, bringing the first physical property prediction sequence and the first physical property label sequence of the current test record into a preset first model loss function to calculate and obtain a corresponding first loss value;

[0078] Wherein, the first model loss function at least includes an L1 loss function, an L2 loss function and a cross entropy loss function;

[0079] Step 26, whether the first loss value satisfies the preset first loss value range is identified; if the first loss value satisfies the first loss value range, whether the current test record is the last first data record of the first test set is identified, if so, go to step 27, if not, extract the next first data record of the first test set as the new current test record and return to step 23; if the first loss value does not satisfy the first loss value range, the first debugging object is identified, and if the first debugging object is a nonlinear regression model, the melting point prediction module, based on the preset first model parameter optimizer, is optimized in the direction of minimizing the first model loss function under the premise that the model parameters of the molecular structure optimization module remain unchanged. The model parameters of the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module are subjected to a round of model parameter modulation and the step 24 is returned to when the round of parameter modulation ends. If the first debugging object is a structure optimization model, the model parameters of the molecular structure optimization module are subjected to a round of model parameter fine-tuning based on a preset second model parameter optimizer in the direction of minimizing the first model loss function under the premise that the model parameters of the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module remain unchanged and the step 24 is returned to when the round of parameter fine-tuning ends.

[0080] Among them, the first loss value range is a preset loss value range; the first model parameter optimizer includes at least an SGD optimizer and an ADAM optimizer; the second model parameter optimizer includes at least an RMSprop optimizer, an AdamW optimizer and an ADAM optimizer;

[0081] Step 27, identifying whether the first debugging object is a structural optimization model; if so, go to step 28; if not, set the first debugging object as a structural optimization model, extract the first first data record of the first test set as a new current test record, and return to step 23;

[0082] Here, it can be seen from the current steps that when the molecular property prediction model is trained, the embodiment of the present invention adopts a two-stage training method to reduce the difficulty of training; 1) in the first stage, the first debugging object is set to be a nonlinear regression model. During the training in this stage, only the parameters of the seven nonlinear regression models (melting point prediction module, boiling point prediction module, vapor pressure prediction module, dielectric constant prediction module, refractive index prediction module, density prediction module and synthesizability prediction module) are modulated, and the model parameters of the Uni-Mol model (molecular structure optimization module) are not processed; 2) in the second stage, the first debugging object is set to be a structure optimization model. During the training in this stage, the parameters of the seven nonlinear regression models (melting point prediction module, boiling point prediction module, vapor pressure prediction module, dielectric constant prediction module, refractive index prediction module, density prediction module and synthesizability prediction module) are not modulated, and only the model parameters of the Uni-Mol model (molecular structure optimization module) are fine-tuned;

[0083] Step 28, traverse all first data records of the first evaluation set; and in the traversal process, use the currently traversed first data record as the corresponding current evaluation record; and form a corresponding second training formula component information from the first training molecular structure type and the first training molecular structure information of the current evaluation record; and input the second training formula component information into the molecular property prediction model to perform seven types of physical property predictions and obtain a corresponding second property prediction sequence; and form a corresponding prediction-label pair from the second property prediction sequence and the first property label sequence of the current test record; and at the end of the traversal, bring all the obtained prediction-label pairs into the preset first model evaluation function to calculate and obtain the corresponding first evaluation value;

[0084] Wherein, the first model evaluation function at least includes the RMSE function;

[0085] Step 29, identifying whether the first evaluation value meets the preset first evaluation value range; if not, returning to step 21 to continue training; if satisfied, stopping training and confirming the end of model training;

[0086] The first evaluation value range is a preset evaluation value range.

[0087] Step 3: After the model training is completed, any electrolyte formula input by the user is received and recorded as the corresponding first formula; and the molecular physical property prediction model is used to predict the physical properties of the electrolyte formula components of the first formula to obtain a corresponding first physical property analysis report and feedback it to the user;

[0088] Specifically comprising: step 31, after the model training is completed, receiving any electrolyte formula input by the user and recording it as the corresponding first formula;

[0089] Wherein, the first formula includes a plurality of first formula component information; the first formula component information includes a first molecular structure type and a first molecular structure information; the first molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first molecular structure type is a one-dimensional type, the corresponding first molecular structure information is a one-dimensional molecular sequence, when the first molecular structure type is a two-dimensional type, the corresponding first molecular structure information is a two-dimensional molecular topology diagram, and when the first molecular structure type is a three-dimensional type, the corresponding first molecular structure information is a three-dimensional molecular conformation;

[0090] Step 32, using the molecular physical property prediction model to predict the physical properties of the electrolyte formula components of the first formula to obtain a corresponding first physical property analysis report and provide feedback to the user;

[0091] The first physical property analysis report includes a plurality of first component analysis records; the first component analysis record includes first formula component information and a corresponding first physical property analysis sequence;

[0092] Specifically, it includes: inputting each first formula component information of the first formula into the molecular physical property prediction model to predict seven types of physical properties and using the physical property prediction sequence obtained this time as the corresponding first physical property analysis sequence; and forming a corresponding first component analysis record from each first formula component information and the corresponding first physical property analysis sequence; and forming a corresponding first physical property analysis report from all the obtained first component analysis records to feedback to the user.

[0093] Figure 3 The module structure diagram of a processing device for predicting the physical properties of electrolyte formula components provided in the second embodiment of the present invention, the device is a terminal device or server that implements the aforementioned method embodiment, and can also be a device that enables the aforementioned terminal device or server to implement the aforementioned method embodiment, for example, the device can be a device or chip system of the aforementioned terminal device or server. Figure 3 As shown, the device includes: a model building module 201, a model training module 202 and a model application module 203.

[0094] The model building module 201 is used to build a molecular property prediction model; the molecular property prediction model is used to predict seven types of physical properties according to the input formula component information and output the corresponding property prediction sequence; wherein the formula component information includes molecular structure type and molecular structure information; the molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the molecular structure type is one-dimensional, the corresponding molecular structure information is a one-dimensional molecular sequence, when the molecular structure type is two-dimensional, the corresponding molecular structure information is a two-dimensional molecular topology map, and when the molecular structure type is three-dimensional, the corresponding molecular structure information is a three-dimensional molecular conformation; the seven types of physical properties include melting point, boiling point, vapor pressure, dielectric constant, refractive index, density and synthesizability; the property prediction sequence includes melting point prediction value, boiling point prediction value, vapor pressure prediction value, dielectric constant prediction value, refractive index prediction value, density prediction value and synthesizability prediction value.

[0095] The model training module 202 is used to perform model training on the molecular property prediction model based on a preset first data set; wherein the first data set includes multiple first data records; the first data record includes a first training molecular structure type, first training molecular structure information and a first physical property label sequence; the first training molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first training molecular structure type is a one-dimensional type, the corresponding first training molecular structure information is a one-dimensional molecular sequence, when the first training molecular structure type is a two-dimensional type, the corresponding first training molecular structure information is a two-dimensional molecular topology map, and when the first training molecular structure type is a three-dimensional type, the corresponding first training molecular structure information is a three-dimensional molecular conformation; the first physical property label sequence includes a first melting point label value, a first boiling point label value, a first vapor pressure label value, a first dielectric constant label value, a first refractive index label value, a first density label value and a first synthesizability label value.

[0096] The model application module 203 is used to receive any electrolyte formula input by the user as the corresponding first formula after the model training is completed; and use the molecular physical property prediction model to predict the physical properties of the electrolyte formula components of the first formula to obtain the corresponding first physical property analysis report and feedback it to the user; wherein, the first formula includes multiple first formula component information; the first formula component information includes a first molecular structure type and a first molecular structure information; the first molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first molecular structure type is a one-dimensional type, the corresponding first molecular structure information is a one-dimensional molecular sequence, when the first molecular structure type is a two-dimensional type, the corresponding first molecular structure information is a two-dimensional molecular topology map, and when the first molecular structure type is a three-dimensional type, the corresponding first molecular structure information is a three-dimensional molecular conformation; the first physical property analysis report includes multiple first component analysis records; the first component analysis record includes the first formula component information and the corresponding first physical property analysis sequence.

[0097] An embodiment of the present invention provides a processing device for predicting the physical properties of electrolyte formula components, which can execute the method steps in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here.

[0098] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the model building module can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The function of the above-mentioned module is determined. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0099] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital signal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module above is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0100] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the above method embodiments are generated. The above computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The above-mentioned computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the above-mentioned computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, Bluetooth, microwave, etc.) methods. The above-mentioned computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The above-mentioned available medium can be a magnetic medium (such as a floppy disk, a hard disk, a tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0101] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. The electronic device may be a terminal device or a server for implementing the method of the aforementioned embodiment, or may be a terminal device or a server for implementing the method of the aforementioned embodiment connected to the aforementioned terminal device or server. Figure 4 As shown, the electronic device may include: a processor 301 (such as a CPU), a memory 302, and a transceiver 303; the transceiver 303 is coupled to the processor 301, and the processor 301 controls the transceiver 303. Various instructions may be stored in the memory 302 to complete various processing functions and implement the processing steps described in the aforementioned embodiment method. Preferably, the electronic device involved in the embodiment of the present invention also includes: a power supply 304, a system bus 305 and a communication port 306. The system bus 305 is used to realize the communication connection between components. The above-mentioned communication port 306 is used for connecting and communicating between the electronic device and other peripherals.

[0102] exist Figure 4The system bus 305 mentioned in the figure can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used to realize the communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM), and may also include non-volatile memory (Non-Volatile Memory), such as at least one disk storage.

[0103] The above-mentioned processor can be a general-purpose processor, including a central processing unit CPU, a network processor (Network Processor, NP), a graphics processing unit (Graphics Processing Unit, GPU), etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0104] It should be noted that an embodiment of the present invention further provides a computer-readable storage medium, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the method and processing process provided in the above embodiments.

[0105] The embodiment of the present invention provides a processing method, device, electronic device and computer-readable storage medium for predicting the physical properties of electrolyte formula components. As can be seen from the above content, the embodiment of the present invention first constructs a molecular property prediction model based on a Uni-Mol model and seven nonlinear regression models, and then through model training, it enables it to predict seven types of physical properties (melting point, boiling point, vapor pressure, dielectric constant, refractive index, density and synthesizability) of one / two / three-dimensional molecular structures and output the corresponding physical property prediction sequence; after the model training is completed, the molecular physical property prediction model is used to analyze the seven types of physical properties of all formula components of any electrolyte formula input by the user and the obtained physical property analysis report is fed back to the user. By performing component physical property analysis on the electrolyte formula through the embodiment of the present invention, the analysis complexity is reduced, the analysis cycle is shortened, and the analysis efficiency is improved.

[0106] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0107] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting the physical properties of electrolyte formulation components, characterized in that: The method comprises: Constructing a molecular property prediction model; the molecular property prediction model is used to predict seven types of physical properties according to the input formula component information and output a corresponding property prediction sequence; wherein the formula component information includes molecular structure type and molecular structure information; the molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the molecular structure type is one-dimensional, the corresponding molecular structure information is a one-dimensional molecular sequence, when the molecular structure type is two-dimensional, the corresponding molecular structure information is a two-dimensional molecular topology map, and when the molecular structure type is three-dimensional, the corresponding molecular structure information is a three-dimensional molecular conformation; the seven types of physical properties include melting point, boiling point, vapor pressure, dielectric constant, refractive index, density and synthesizability; the property prediction sequence includes melting point prediction value, boiling point prediction value, vapor pressure prediction value, dielectric constant prediction value, refractive index prediction value, density prediction value and synthesizability prediction value; Model training is performed on the molecular property prediction model based on a preset first data set; wherein the first data set includes a plurality of first data records; the first data record includes a first training molecular structure type, first training molecular structure information and a first physical property label sequence; the first training molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first training molecular structure type is a one-dimensional type, the corresponding first training molecular structure information is a one-dimensional molecular sequence, when the first training molecular structure type is a two-dimensional type, the corresponding first training molecular structure information is a two-dimensional molecular topology map, and when the first training molecular structure type is a three-dimensional type, the corresponding first training molecular structure information is a three-dimensional molecular conformation; the first physical property label sequence includes a first melting point label value, a first boiling point label value, a first vapor pressure label value, a first dielectric constant label value, a first refractive index label value, a first density label value and a first synthesizability label value; After the model training is completed, any electrolyte formula input by the user is received and recorded as the corresponding first formula; and the molecular physical property prediction model is used to predict the physical properties of the electrolyte formula components of the first formula to obtain a corresponding first physical property analysis report to be fed back to the user; wherein, the first formula includes multiple first formula component information; the first formula component information includes a first molecular structure type and a first molecular structure information; the first molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first molecular structure type is a one-dimensional type, the corresponding first molecular structure information is a one-dimensional molecular sequence, when the first molecular structure type is a two-dimensional type, the corresponding first molecular structure information is a two-dimensional molecular topology map, and when the first molecular structure type is a three-dimensional type, the corresponding first molecular structure information is a three-dimensional molecular conformation; the first physical property analysis report includes multiple first component analysis records; the first component analysis record includes the first formula component information and the corresponding first physical property analysis sequence.

2. The method for predicting the physical properties of electrolyte formulation components according to claim 1, characterized in that: The model input end of the molecular physical property prediction model is used to receive the input formula component information, and the model output end is used to output the corresponding physical property prediction sequence; The molecular property prediction model includes a molecular structure initialization module, a molecular structure optimization module, a melting point prediction module, a boiling point prediction module, a vapor pressure prediction module, a dielectric constant prediction module, a refractive index prediction module, a density prediction module, a synthesizability prediction module and a molecular property output module; The input end of the molecular structure initialization module is connected to the input end of the model, and the output end is connected to the input end of the molecular structure optimization module; the output end of the molecular structure optimization module is respectively connected to the input ends of the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module; the seven input ends of the molecular property output module are respectively connected to the output ends of the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module, and the output section of the molecular property output module is connected to the model output end; The molecular structure initialization module is used to extract the corresponding molecular structure type and the molecular structure information from the formula ingredient information as the corresponding current type and current structure information; and identify the current type; If the current type is a one-dimensional type, the current structure information is used as the corresponding first molecular sequence, and a one-dimensional sequence processing interface provided by a preset chemical informatics tool is used to perform three-dimensional molecular structure modeling processing according to the first molecular sequence to obtain a corresponding initialized molecular conformation; If the current type is a two-dimensional type, the current structure information is used as the corresponding first molecular topology map, and a two-dimensional topology map processing interface provided by the chemical informatics tool is used to perform three-dimensional molecular structure modeling processing according to the first molecular topology map to obtain the corresponding initialized molecular conformation; If the current type is a three-dimensional type, the current structure information is used as the corresponding initialized molecular conformation; and sending the obtained initialized molecular conformation to the molecular structure optimization module; wherein the chemical informatics tool at least includes OpenBabel and RDK it; The one-dimensional sequence processing interface is a processing interface provided by the chemical informatics tool, and the one-dimensional sequence processing interface is used to perform a corresponding three-dimensional molecular conformation conversion according to the one-dimensional molecular sequence input by the interface and output the obtained three-dimensional molecular conformation as interface output data; the two-dimensional topological map processing interface is a processing interface provided by the chemical informatics tool, and the two-dimensional topological map processing interface is used to perform a corresponding three-dimensional molecular conformation conversion according to the two-dimensional molecular topological map input by the interface and output the obtained three-dimensional molecular conformation as interface output data; The molecular structure optimization module is implemented based on a Uni-Mo l model that has completed model pre-training; the molecular structure optimization module is used to perform three-dimensional structural optimization processing on the initialized molecular conformation to obtain a corresponding optimized molecular conformation; and the optimized molecular conformation is sent to the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module respectively; The melting point prediction module is implemented based on a first nonlinear regression model; the melting point prediction module is used to perform melting point prediction processing according to the optimized molecular conformation to obtain the corresponding melting point prediction value and send it to the molecular property output module; the first nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model; The boiling point prediction module is implemented based on a second nonlinear regression model; the boiling point prediction module is used to perform boiling point prediction processing according to the optimized molecular conformation to obtain the corresponding boiling point prediction value and send it to the molecular property output module; the second nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model; The vapor pressure prediction module is implemented based on a third nonlinear regression model; the vapor pressure prediction module is used to perform vapor pressure prediction processing according to the optimized molecular conformation to obtain the corresponding vapor pressure prediction value and send it to the molecular property output module; the third nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model; The dielectric constant prediction module is implemented based on a fourth nonlinear regression model; the dielectric constant prediction module is used to perform dielectric constant prediction processing according to the optimized molecular conformation to obtain the corresponding dielectric constant prediction value and send it to the molecular property output module; the fourth nonlinear regression model at least includes an XGBoost model, a GBDT model, a RandomForest model and an MLP model; The refractive index prediction module is implemented based on the fifth nonlinear regression model; the refractive index prediction module is used to perform refractive index prediction processing according to the optimized molecular conformation to obtain the corresponding refractive index prediction value and send it to the molecular property output module; the fifth nonlinear regression model at least includes XGBoost model, GBDT model, Random Forest model and MLP model; The density prediction module is implemented based on the sixth nonlinear regression model; the density prediction module is used to perform density prediction processing according to the optimized molecular conformation to obtain the corresponding density prediction value and send it to the molecular property output module; the sixth nonlinear regression model at least includes an XGBoost model, a GBDT model, a Random Forest model and an MLP model; The synthesizability prediction module is implemented based on the seventh nonlinear regression model; the synthesizability prediction module is used to perform synthesizability prediction processing according to the optimized molecular conformation to obtain the corresponding synthesizability prediction value and send it to the molecular property output module; the seventh nonlinear regression model at least includes an XGBoost model, a GBDT model, a RandomForest model and an MLP model; The molecular property output module is used to compose and output the corresponding property prediction sequence from the obtained melting point prediction value, boiling point prediction value, vapor pressure prediction value, dielectric constant prediction value, refractive index prediction value, density prediction value and synthesizability prediction value.

3. The method for predicting the physical properties of electrolyte formulation components according to claim 2, characterized in that: The performing model training on the molecular property prediction model based on the preset first data set specifically includes: Step 31, randomly splitting the first data set based on a preset first test evaluation ratio to obtain a corresponding first test set and a first evaluation set; Wherein, both the first test set and the first evaluation set are composed of a plurality of the first data records; the ratio of the total number of records in the first test set to the total number of records in the first evaluation set satisfies the first test evaluation ratio; Step 32, setting the corresponding first debugging object as a nonlinear regression model; and extracting the first first data record of the first test set as the corresponding current test record; Wherein, the first debugging object includes a nonlinear regression model and a structural optimization model; Step 33, forming a corresponding first training formula ingredient information from the first training molecular structure type and the first training molecular structure information of the current test record; Step 34, inputting the first training formula ingredient information into the molecular physical property prediction model to perform seven types of physical property predictions and obtain a corresponding first physical property prediction sequence; Wherein, the first physical property prediction sequence includes a first melting point prediction value, a first boiling point prediction value, a first vapor pressure prediction value, a first dielectric constant prediction value, a first refractive index prediction value, a first density prediction value and a first synthesizability prediction value; Step 35, bringing the first physical property prediction sequence and the first physical property label sequence of the current test record into a preset first model loss function to calculate and obtain a corresponding first loss value; Wherein, the first model loss function at least includes an L1 loss function, an L2 loss function and a cross entropy loss function; Step 36, identifying whether the first loss value satisfies a preset first loss value range; if the first loss value satisfies the first loss value range, identifying whether the current test record is the last first data record of the first test set, if so, go to step 37, otherwise extract the next first data record of the first test set as the new current test record and return to step 33; if the first loss value does not satisfy the first loss value range, identifying the first debugging object, if the first debugging object is a nonlinear regression model, under the premise that the model parameters of the molecular structure optimization module remain unchanged, the melting point prediction module, based on the preset first model parameter optimizer, is optimized in the direction of minimizing the first model loss function. The model parameters of the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module are subjected to a round of model parameter modulation and the step 34 is returned when the current round of parameter modulation is completed. If the first debugging object is a structure optimization model, the model parameters of the molecular structure optimization module are subjected to a round of model parameter fine-tuning based on a preset second model parameter optimizer in the direction of minimizing the first model loss function under the premise that the model parameters of the melting point prediction module, the boiling point prediction module, the vapor pressure prediction module, the dielectric constant prediction module, the refractive index prediction module, the density prediction module and the synthesizability prediction module remain unchanged, and the step 34 is returned when the current round of parameter fine-tuning is completed. Wherein, the first model parameter optimizer includes at least an SGD optimizer and an ADAM optimizer; the second model parameter optimizer includes at least an RMSprop optimizer, an AdamW optimizer and an ADAM optimizer; Step 37, identifying whether the first debugging object is a structural optimization model; if so, going to step 38; if not, setting the first debugging object as a structural optimization model, extracting the first first data record of the first test set as the new current test record, and returning to step 33; Step 38, traverse all the first data records of the first evaluation set; and in the traversal process, use the first data record currently traversed as the corresponding current evaluation record; and form a corresponding second training formula ingredient information from the first training molecular structure type and the first training molecular structure information of the current evaluation record; and input the second training formula ingredient information into the molecular property prediction model to perform seven types of physical property predictions and obtain a corresponding second property prediction sequence; and form a corresponding prediction-label pair from the second property prediction sequence and the first property label sequence of the current test record; and at the end of the traversal, bring all the obtained prediction-label pairs into the preset first model evaluation function to calculate and obtain the corresponding first evaluation value; Wherein, the first model evaluation function at least includes an RMSE function; Step 39, identifying whether the first evaluation value meets the preset first evaluation value range; if not, returning to step 31 to continue training; if satisfied, stopping training and confirming the end of model training.

4. The method for predicting the physical properties of electrolyte formulation components according to claim 1, characterized in that: The using the molecular physical property prediction model to perform physical property prediction processing on the first formula for electrolyte formula components to obtain a corresponding first physical property analysis report and feed it back to the user specifically includes: Input the first formula component information of each of the first formula into the molecular physical property prediction model to predict seven types of physical properties and use the physical property prediction sequence obtained this time as the corresponding first physical property analysis sequence; and each of the first formula component information and the corresponding first physical property analysis sequence constitute a corresponding first component analysis record; and all the first component analysis records obtained constitute a corresponding first physical property analysis report to be fed back to the user.

5. A device for executing the processing method for predicting the physical properties of electrolyte formula components according to any one of claims 1 to 4, characterized in that: The device comprises: a model building module, a model training module and a model application module; The model building module is used to build a molecular property prediction model; the molecular property prediction model is used to predict seven types of physical properties according to the input formula component information and output the corresponding property prediction sequence; wherein the formula component information includes molecular structure type and molecular structure information; the molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the molecular structure type is one-dimensional, the corresponding molecular structure information is a one-dimensional molecular sequence, when the molecular structure type is two-dimensional, the corresponding molecular structure information is a two-dimensional molecular topology map, and when the molecular structure type is three-dimensional, the corresponding molecular structure information is a three-dimensional molecular conformation; the seven types of physical properties include melting point, boiling point, vapor pressure, dielectric constant, refractive index, density and synthesizability; the property prediction sequence includes melting point prediction value, boiling point prediction value, vapor pressure prediction value, dielectric constant prediction value, refractive index prediction value, density prediction value and synthesizability prediction value; The model training module is used to perform model training on the molecular property prediction model based on a preset first data set; wherein the first data set includes multiple first data records; the first data record includes a first training molecular structure type, first training molecular structure information and a first physical property label sequence; the first training molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first training molecular structure type is a one-dimensional type, the corresponding first training molecular structure information is a one-dimensional molecular sequence, when the first training molecular structure type is a two-dimensional type, the corresponding first training molecular structure information is a two-dimensional molecular topology map, and when the first training molecular structure type is a three-dimensional type, the corresponding first training molecular structure information is a three-dimensional molecular conformation; the first physical property label sequence includes a first melting point label value, a first boiling point label value, a first vapor pressure label value, a first dielectric constant label value, a first refractive index label value, a first density label value and a first synthesizability label value; The model application module is used to receive any electrolyte formula input by the user as the corresponding first formula after the model training is completed; and use the molecular physical property prediction model to predict the physical properties of the electrolyte formula components of the first formula to obtain the corresponding first physical property analysis report and feedback it to the user; wherein, the first formula includes multiple first formula component information; the first formula component information includes a first molecular structure type and a first molecular structure information; the first molecular structure type includes one-dimensional, two-dimensional and three-dimensional types; when the first molecular structure type is a one-dimensional type, the corresponding first molecular structure information is a one-dimensional molecular sequence, when the first molecular structure type is a two-dimensional type, the corresponding first molecular structure information is a two-dimensional molecular topology map, and when the first molecular structure type is a three-dimensional type, the corresponding first molecular structure information is a three-dimensional molecular conformation; the first physical property analysis report includes multiple first component analysis records; the first component analysis record includes the first formula component information and the corresponding first physical property analysis sequence.

6. An electronic device, characterized in that: include: memory, processors, and transceivers; The processor is used to couple with the memory, read and execute instructions in the memory, so as to implement the method according to any one of claims 1 to 4; The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a computer, the computer is enabled to execute the method according to any one of claims 1 to 4.

Images