Electrolyte formula property prediction method and device based on graph neural network

Abstract

The invention discloses an electrolyte formula property prediction method and device based on a graph neural network, and the method comprises the steps: generating target electrolyte virtual molecules through molecular nodes in a target electrolyte formula according to the architecture of atomic nodes in an original molecular graph; constructing and training a pre-training model based on the open source model; constructing and training a property prediction model based on the graph neural network; inputting the target electrolyte virtual molecules into the trained pre-training model to obtain features of the target electrolyte virtual molecules; inputting the characteristics of the virtual molecules of the target electrolyte into the trained property prediction model; and obtaining the properties of the target electrolyte formula. According to the method, the relatively high-precision formula property can be obtained in an extremely short time, and the electrolyte formula obtaining efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrolyte formula property prediction steel, and in particular to a method and device for predicting electrolyte formula properties based on a graph neural network. Background Art

[0002] Formula property prediction is a critical step in the research and development of battery electrolytes. This technology can quickly help researchers obtain the properties of the current formula and accelerate the research and development of battery electrolytes.

[0003] At present, researchers mainly rely on human experience and a large number of experiments to obtain the true properties of a formula. Human experience can help screen out a large number of unreliable formulas, and then researchers can conduct experiments on the remaining electrolyte formulas. This method is time-consuming and labor-intensive.

[0004] Therefore, how to invent a method for predicting the properties of electrolyte formulas that can obtain relatively high-precision formula properties in a very short time and improve the efficiency of obtaining electrolyte formulas has become an urgent problem to be solved. Summary of the invention

[0005] To this end, the present invention provides a method and device for predicting electrolyte formula properties based on graph neural network, which not only inherits the experience of experts in this field, but also can obtain relatively high-precision formula properties in a very short time. Then researchers only need to experiment with several better groups of formulas to obtain the formula they want.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for predicting electrolyte formula properties based on graph neural network, comprising: According to the structure of the atomic nodes in the original molecular graph, the target electrolyte virtual molecules are generated through the molecular nodes in the target electrolyte formula; Based on the open source model, a pre-trained model is constructed; the pre-trained model is trained by setting a training data set to obtain a trained pre-trained model; Based on the graph neural network, a property prediction model is constructed; the property prediction model is trained by using the set training data set to obtain a trained property prediction model; Inputting the target electrolyte virtual molecule into the trained pre-trained model; obtaining the characteristics of the target electrolyte virtual molecule through processing by the trained pre-trained model; The characteristics of the target electrolyte virtual molecule are input into the trained property prediction model; and the properties of the target electrolyte formula are obtained through prediction processing by the trained property prediction model.

[0007] As a preferred scheme of the electrolyte formula property prediction method based on graph neural network, in the process of training the pre-trained model and the property prediction model through the set training data set, the set training data set is divided into a training set, a validation set and a test set according to a set ratio.

[0008] As a preferred scheme of the electrolyte formula property prediction method based on graph neural network, the characteristics of the target electrolyte virtual molecule include low-dimensional atom-level characteristics and Bond-level characteristics; in the process of obtaining the low-dimensional atom-level characteristics, the target electrolyte virtual molecule is subjected to molecular characterization processing by the trained pre-trained model to obtain high-dimensional atom-level characteristics; the high-dimensional atom-level characteristics are processed by setting a dimensionality reduction algorithm to obtain the low-dimensional atom-level characteristics.

[0009] As a preferred solution of the electrolyte formula property prediction method based on graph neural network, in the process of obtaining the Bond level features, the Bond level features include simulated connection relationship between molecules, non-covalent interaction energy and VIP vertical ionization energy; The calculation formula of the non-covalent interaction energy is: ; In the formula, IE is the non-covalent interaction energy; ΔE_els is the classical electrostatic interaction energy between fragments, els represents electrostatic interaction; ΔE_x is the exchange energy, x represents exchange interactions; ΔE_dc is the dispersion correction energy; ΔE_orb is the orbital interaction energy; The calculation formula of the VIP vertical ionization energy is: ; ; ; In the formula, is the VIP vertical ionization energy; 、 are the masses of molecules; 、 The energy of a molecule after losing an electron. 、 All are the complete energies of the molecules; is the vertical ionization energy of molecule 1; is the vertical ionization energy of molecule 2.

[0010] As a preferred scheme of the electrolyte formula property prediction method based on graph neural network, in the process of training the property prediction model through the set training data set to obtain the trained property prediction model, the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules in the set training data set are learned through the graph neural network, and features and property labels are output; by performing label verification on the features and property labels, the parameters of the graph neural network are iteratively optimized until the set training times are reached to obtain the trained property prediction model.

[0011] The present invention also provides an electrolyte formula property prediction device based on a graph neural network, based on the above electrolyte formula property prediction method based on a graph neural network, comprising: A target electrolyte virtual molecule generation module is used to generate a target electrolyte virtual molecule according to the structure of the atomic nodes in the original molecular graph and through the molecular nodes in the target electrolyte formula; A pre-trained model construction and training module is used to construct a pre-trained model based on an open source model; the pre-trained model is trained by setting a training data set to obtain a trained pre-trained model; A property prediction model construction and training module is used to construct a property prediction model based on a graph neural network; the property prediction model is trained by the set training data set to obtain a trained property prediction model; A molecular feature acquisition module, used to input the target electrolyte virtual molecule into the trained pre-trained model; and obtain the features of the target electrolyte virtual molecule through processing by the trained pre-trained model; The electrolyte formula property acquisition module is used to input the characteristics of the target electrolyte virtual molecule into the trained property prediction model; and obtain the properties of the target electrolyte formula through prediction processing by the trained property prediction model.

[0012] As a preferred scheme of the electrolyte formula property prediction device based on graph neural network, in the pre-training model construction and training module and the property prediction model construction and training module, in the process of training the pre-training model and the property prediction model through the set training data set, the set training data set is divided into a training set, a validation set and a test set according to a set ratio.

[0013] As a preferred solution of the electrolyte formula property prediction device based on graph neural network, in the molecular feature acquisition module, the characteristics of the target electrolyte virtual molecule include low-dimensional atom-level characteristics and Bond-level characteristics; in the process of acquiring the low-dimensional atom-level characteristics, the target electrolyte virtual molecule is subjected to molecular characterization processing by the trained pre-trained model to obtain high-dimensional atom-level characteristics; the high-dimensional atom-level characteristics are processed by setting a dimensionality reduction algorithm to obtain the low-dimensional atom-level characteristics.

[0014] As a preferred solution of the electrolyte formula property prediction device based on graph neural network, in the molecular feature acquisition module, in the process of acquiring the Bond level features, the Bond level features include simulated connection relationship between molecules, non-covalent interaction energy and VIP vertical ionization energy; The calculation formula of the non-covalent interaction energy is: ; In the formula, IE is the non-covalent interaction energy; ΔE_els is the classical electrostatic interaction energy between fragments, els represents electrostatic interaction; ΔE_x is the exchange energy, x represents exchange interactions; ΔE_dc is the dispersion correction energy; ΔE_orb is the orbital interaction energy; The calculation formula of the VIP vertical ionization energy is: ; ; ; In the formula, is the VIP vertical ionization energy; 、 are the masses of molecules; 、 The energy of a molecule after losing an electron. 、 All are the complete energies of the molecules; is the vertical ionization energy of molecule 1; is the vertical ionization energy of molecule 2.

[0015] As a preferred solution of the electrolyte formula property prediction device based on graph neural network, in the property prediction model construction and training module, in the process of training the property prediction model through the set training data set to obtain the trained property prediction model, the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules in the set training data set are learned through the graph neural network, and features and property labels are output; by performing label verification on the features and property labels, the parameters of the graph neural network are iteratively optimized until the set training times are reached to obtain the trained property prediction model.

[0016] The present invention has the following advantages: according to the architecture of the atomic nodes in the original molecular graph, the target electrolyte virtual molecules are generated through the molecular nodes in the target electrolyte formula; based on the open source model, a pre-trained model is constructed; the pre-trained model is trained by setting a training data set to obtain a trained pre-trained model; based on the graph neural network, a property prediction model is constructed; the property prediction model is trained by setting a training data set to obtain a trained property prediction model; the target electrolyte virtual molecules are input into the trained pre-trained model; the characteristics of the target electrolyte virtual molecules are obtained by processing the trained pre-trained model; the characteristics of the target electrolyte virtual molecules are input into the trained property prediction model; the properties of the target electrolyte formula are obtained by predicting and processing the trained property prediction model. The present invention not only inherits the experience of experts in this field, but also can obtain relatively high-precision formula properties in a very short time, and then researchers only need to experiment on several better groups of formulas to obtain the formulas they want. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0018] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0019] Figure 1A schematic flow chart of a method for predicting properties of electrolyte formulations based on a graph neural network provided in Example 1 of the present invention; Figure 2 A schematic diagram of a specific implementation process of a method for predicting electrolyte formulation properties based on a graph neural network provided in Example 1 of the present invention; Figure 3 A schematic diagram of a virtual molecule of an electrolyte in a method for predicting electrolyte formulation properties based on a graph neural network provided in Example 1 of the present invention; Figure 4 A schematic diagram of a flow chart of a property prediction model construction process in a method for predicting properties of electrolyte formula based on a graph neural network provided in Example 1 of the present invention; Figure 5 Schematic diagram of the architecture of an electrolyte formula property prediction device based on a graph neural network provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0020] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of 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.

[0021] Example 1 See also Figure 1 and Figure 2 Embodiment 1 of the present invention provides a method for predicting electrolyte formula properties based on graph neural network, comprising the following steps: S1. Generate a target electrolyte virtual molecule through the molecular nodes in the target electrolyte formula according to the structure of the atomic nodes in the original molecular graph; S2. Based on the open source model, a pre-trained model is constructed; the pre-trained model is trained by setting a training data set to obtain a trained pre-trained model; S3. Based on the graph neural network, construct a property prediction model; train the property prediction model by using the set training data set to obtain a trained property prediction model; S4, inputting the target electrolyte virtual molecule into the trained pre-trained model; and obtaining the characteristics of the target electrolyte virtual molecule through processing by the trained pre-trained model; S5. Input the characteristics of the target electrolyte virtual molecule into the trained property prediction model; obtain the properties of the target electrolyte formula through prediction processing by the trained property prediction model.

[0022] In this embodiment, in step S1, according to the structure of the atomic nodes in the original molecular graph, the target electrolyte virtual molecules are generated through the molecular nodes in the target electrolyte formula; Specifically, traditional molecular graphs are composed of atomic nodes, while the electrolyte formula virtual molecules are composed of electrolyte molecule nodes. Figure 3 shown.

[0023] In this embodiment, in step S2, a pre-trained model is constructed based on an open source model; the pre-trained model is trained by setting a training data set to obtain a trained pre-trained model; Specifically, the pre-trained model can adopt an open source model, or it can be built and trained based on an open source model. Open source models include Uni-mol, chemformer, etc. The training data set is set to include the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules; the training data set is set to be divided into a training set, a validation set, and a test set in a ratio of 8:1:1. The pre-trained model is trained using the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules, so that the trained pre-trained model can extract the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules.

[0024] In this embodiment, in step S3, a property prediction model is constructed based on a graph neural network; the property prediction model is trained by the set training data set to obtain a trained property prediction model; Specifically, Figure 4 As shown, based on the graph neural network, a property prediction model is constructed; the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules are input into traditional graph neural networks such as GNN or MPNN in the form of molecular graphs, and the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules are learned through the graph neural network, and the features and property labels are output; by performing label verification on the features and property labels, the parameters of the graph neural network are iteratively optimized until the set number of training times is reached, and the trained property prediction model is obtained.

[0025] In this embodiment, in step S4, the target electrolyte virtual molecule is input into the trained pre-trained model; the characteristics of the target electrolyte virtual molecule are obtained by processing the trained pre-trained model; Specifically, the characteristics of the target electrolyte virtual molecule include low-dimensional atom-level characteristics and Bond-level characteristics; in the process of obtaining the low-dimensional atom-level characteristics, the target electrolyte virtual molecule is subjected to molecular characterization processing by the trained pre-trained model to obtain high-dimensional atom-level characteristics; the high-dimensional atom-level characteristics are processed by setting a dimensionality reduction algorithm to obtain the low-dimensional atom-level characteristics.

[0026] In the process of obtaining the Bond level features, the Bond level features include simulated connection relationships between molecules, non-covalent interaction energy and VIP vertical ionization energy; The calculation formula of the non-covalent interaction energy is: ; In the formula, IE is the non-covalent interaction energy; ΔE_els is the classical electrostatic interaction energy between fragments, els represents electrostatic interaction; ΔE_x is the exchange energy, x represents exchange interactions; ΔE_dc is the dispersion correction energy; ΔE_orb is the orbital interaction energy; The calculation formula of the VIP vertical ionization energy is: ; ; ; In the formula, is the VIP vertical ionization energy; , are the masses of molecules; , The energy of a molecule after losing an electron. , All are the complete energies of the molecules; is the vertical ionization energy of molecule 1; is the vertical ionization energy of molecule 2.

[0027] In this embodiment, in step S5, the characteristics of the target electrolyte virtual molecule are input into the trained property prediction model; and the properties of the target electrolyte formula are obtained through prediction processing by the trained property prediction model.

[0028] Specifically, the characteristics of the target electrolyte virtual molecule are input into the trained property prediction model for prediction to obtain the properties of the target electrolyte formula.

[0029] In a possible embodiment, a specific recipe property prediction example is provided as follows: The researchers need to be given a formula A, which contains 6 molecules. Through the transformation of step S1 of the present invention, a formula virtual molecule A is generated, which contains 6 nodes.

[0030] First, the six nodes are input into the pre-trained model Uni-mol to obtain atom-level features and bond-level features. Among them, the atom feature dimension obtained by Uni-mol is relatively high. In order to reduce the difficulty of model training, the PCA algorithm is used to reduce the dimension of this part of the feature. The combination of the two constitutes the molecular graph feature of the virtual molecule A.

[0031] Then, the atom features and Bond features of each node after dimensionality reduction are taken as input and input into the property prediction model, and finally the properties of formula A can be obtained.

[0032] In summary, the present invention generates a target electrolyte virtual molecule according to the architecture of the atomic nodes in the original molecular graph and through the molecular nodes in the target electrolyte formula; constructs a pre-trained model based on an open source model; trains the pre-trained model by setting a training data set to obtain a trained pre-trained model; constructs a property prediction model based on a graph neural network; trains the property prediction model by setting a training data set to obtain a trained property prediction model; inputs the target electrolyte virtual molecule into the trained pre-trained model; obtains the characteristics of the target electrolyte virtual molecule through processing by the trained pre-trained model; inputs the characteristics of the target electrolyte virtual molecule into the trained property prediction model; obtains the properties of the target electrolyte formula through prediction processing by the trained property prediction model. The present invention not only inherits the experience of experts in this field, but also can obtain relatively high-precision formula properties in a very short time, and then researchers only need to experiment on several better groups of formulas to obtain the formulas they want.

[0033] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0034] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0035] Example 2 See also Figure 5 Embodiment 2 of the present invention further provides an electrolyte formula property prediction device based on a graph neural network, comprising: The target electrolyte virtual molecule generation module 001 is used to generate the target electrolyte virtual molecule according to the structure of the atomic nodes in the original molecular graph and through the molecular nodes in the target electrolyte formula; The pre-training model construction and training module 002 is used to construct a pre-training model based on the open source model; train the pre-training model by setting a training data set to obtain a trained pre-training model; The property prediction model construction and training module 003 is used to construct a property prediction model based on a graph neural network; the property prediction model is trained by the set training data set to obtain a trained property prediction model; The molecular feature acquisition module 004 is used to input the target electrolyte virtual molecule into the trained pre-trained model; and obtain the feature of the target electrolyte virtual molecule through processing by the trained pre-trained model; The electrolyte formula property acquisition module 005 is used to input the characteristics of the target electrolyte virtual molecule into the trained property prediction model; and obtain the properties of the target electrolyte formula through prediction processing by the trained property prediction model.

[0036] In this embodiment, in the pre-trained model construction and training module 002 and the property prediction model construction and training module 003, in the process of training the pre-trained model and the property prediction model through the set training data set, the set training data set is divided into a training set, a validation set and a test set according to a set ratio.

[0037] In this embodiment, in the molecular feature acquisition module 004, the features of the target electrolyte virtual molecule include low-dimensional atom-level features and Bond-level features; in the process of acquiring the low-dimensional atom-level features, the target electrolyte virtual molecule is subjected to molecular characterization processing by the trained pre-trained model to obtain high-dimensional atom-level features; the high-dimensional atom-level features are processed by setting a dimensionality reduction algorithm to obtain the low-dimensional atom-level features.

[0038] In this embodiment, in the molecular feature acquisition module 004, in the process of acquiring the Bond level features, the Bond level features include simulated connection relationships between molecules, non-covalent interaction energy and VIP vertical ionization energy; The calculation formula of the non-covalent interaction energy is: ; In the formula, IE is the non-covalent interaction energy; ΔE_els is the classical electrostatic interaction energy between fragments, els represents electrostatic interaction; ΔE_x is the exchange energy, x represents exchange interactions; ΔE_dc is the dispersion correction energy; ΔE_orb is the orbital interaction energy; The calculation formula of the VIP vertical ionization energy is: ; ; ; In the formula, is the VIP vertical ionization energy; 、 are the masses of molecules; 、 The energy of a molecule after losing an electron. 、 All are the complete energies of the molecules; is the vertical ionization energy of molecule 1; is the vertical ionization energy of molecule 2.

[0039] In this embodiment, in the property prediction model construction and training module 003, in the process of training the property prediction model through the set training data set to obtain the trained property prediction model, the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules in the set training data set are learned through the graph neural network, and features and property labels are output; by performing label verification on the features and property labels, the parameters of the graph neural network are iteratively optimized until the set training times are reached to obtain the trained property prediction model.

[0040] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0041] Example 3 Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a method for predicting the properties of an electrolyte formula based on a graph neural network is stored. The program code includes instructions for executing embodiment 1 or any possible implementation thereof.

[0042] Computer-readable storage media 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. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0043] Example 4 Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor; The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute an electrolyte formula property prediction method based on a graph neural network according to Example 1 or any possible implementation thereof.

[0044] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0045] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part 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, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0046] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0047] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for predicting electrolyte formula properties based on graph neural network, characterized in that: include: According to the structure of the atomic nodes in the original molecular graph, the target electrolyte virtual molecules are generated through the molecular nodes in the target electrolyte formula; Build pre-trained models based on open source models; The pre-trained model is trained by setting a training data set to obtain a trained pre-trained model; Based on the graph neural network, a property prediction model is constructed; the property prediction model is trained by using the set training data set to obtain a trained property prediction model; Inputting the target electrolyte virtual molecule into the trained pre-trained model; obtaining the characteristics of the target electrolyte virtual molecule through processing by the trained pre-trained model; The characteristics of the target electrolyte virtual molecule are input into the trained property prediction model; and the properties of the target electrolyte formula are obtained through prediction processing by the trained property prediction model.

2. The method for predicting electrolyte formulation properties based on graph neural network according to claim 1, characterized in that: In the process of training the pre-training model and the property prediction model by using the set training data set, the set training data set is divided into a training set, a validation set and a test set according to a set ratio.

3. The method for predicting electrolyte formulation properties based on graph neural network according to claim 2, characterized in that: The characteristics of the target electrolyte virtual molecule include low-dimensional atom-level characteristics and Bond-level characteristics; in the process of obtaining the low-dimensional atom-level characteristics, the target electrolyte virtual molecule is subjected to molecular characterization processing by the trained pre-trained model to obtain high-dimensional atom-level characteristics; the high-dimensional atom-level characteristics are processed by setting a dimensionality reduction algorithm to obtain the low-dimensional atom-level characteristics.

4. The method for predicting electrolyte formulation properties based on graph neural network according to claim 3, characterized in that: In the process of obtaining the Bond level features, the Bond level features include simulated connection relationships between molecules, non-covalent interaction energy and VIP vertical ionization energy; The calculation formula of the non-covalent interaction energy is: ； In the formula, IE is the non-covalent interaction energy; ΔE_els is the classical electrostatic interaction energy between fragments, els represents electrostatic interaction; ΔE_x is the exchange energy, x represents exchange interactions; ΔE_dc is the dispersion correction energy; ΔE_orb is the orbital interaction energy; The calculation formula of the VIP vertical ionization energy is: ； ； ； In the formula, is the VIP vertical ionization energy; , are the masses of molecules; , The energy of a molecule after losing an electron. , All are the complete energies of the molecules; is the vertical ionization energy of molecule 1; is the vertical ionization energy of molecule 2.

5. The method for predicting electrolyte formulation properties based on graph neural network according to claim 4, characterized in that: In the process of training the property prediction model through the set training data set to obtain the trained property prediction model, the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules in the set training data set are learned through a graph neural network, and features and property labels are output; by performing label verification on the features and property labels, the parameters of the graph neural network are iteratively optimized until the set number of training times is reached to obtain the trained property prediction model.

6. An electrolyte formula property prediction device based on a graph neural network, using the electrolyte formula property prediction method based on a graph neural network according to any one of claims 1 to 5, characterized in that: include: A target electrolyte virtual molecule generation module is used to generate a target electrolyte virtual molecule according to the structure of the atomic nodes in the original molecular graph and through the molecular nodes in the target electrolyte formula; Pre-trained model construction and training module, used to build pre-trained models based on open source models; The pre-trained model is trained by setting a training data set to obtain a trained pre-trained model; A property prediction model construction and training module is used to construct a property prediction model based on a graph neural network; the property prediction model is trained by the set training data set to obtain a trained property prediction model; A molecular feature acquisition module, used to input the target electrolyte virtual molecule into the trained pre-trained model; and obtain the features of the target electrolyte virtual molecule through processing by the trained pre-trained model; The electrolyte formula property acquisition module is used to input the characteristics of the target electrolyte virtual molecule into the trained property prediction model; and obtain the properties of the target electrolyte formula through prediction processing by the trained property prediction model.

7. The electrolyte formula property prediction device based on graph neural network according to claim 6 is characterized in that: In the pre-trained model construction and training module and the property prediction model construction and training module, in the process of training the pre-trained model and the property prediction model through the set training data set, the set training data set is divided into a training set, a validation set and a test set according to a set ratio.

8. The electrolyte formula property prediction device based on graph neural network according to claim 7 is characterized in that: In the molecular feature acquisition module, the features of the target electrolyte virtual molecule include low-dimensional atom-level features and Bond-level features; in the process of acquiring the low-dimensional atom-level features, the target electrolyte virtual molecule is subjected to molecular characterization processing by means of the trained pre-trained model to obtain high-dimensional atom-level features; the high-dimensional atom-level features are processed by setting a dimensionality reduction algorithm to obtain the low-dimensional atom-level features.

9. The electrolyte formula property prediction device based on graph neural network according to claim 8, characterized in that: In the molecular feature acquisition module, in the process of acquiring the Bond level features, the Bond level features include simulated connection relationships between molecules, non-covalent interaction energy and VIP vertical ionization energy; The calculation formula of the non-covalent interaction energy is: ； In the formula, IE is the non-covalent interaction energy; ΔE_els is the classical electrostatic interaction energy between fragments, els represents electrostatic interaction; ΔE_x is the exchange energy, x represents exchange interactions; ΔE_dc is the dispersion correction energy; ΔE_orb is the orbital interaction energy; The calculation formula of the VIP vertical ionization energy is: ； ； ； In the formula, is the VIP vertical ionization energy; 、 are the masses of molecules; 、 The energy of a molecule after losing an electron. 、 All are the complete energies of the molecules; is the vertical ionization energy of molecule 1; is the vertical ionization energy of molecule 2.

10. The electrolyte formula property prediction device based on graph neural network according to claim 9, characterized in that: In the property prediction model construction and training module, in the process of training the property prediction model through the set training data set to obtain the trained property prediction model, the low-dimensional atom-level features and Bond-level features of the target electrolyte virtual molecules in the set training data set are learned through the graph neural network, and features and property labels are output; by performing label verification on the features and property labels, the parameters of the graph neural network are iteratively optimized until the set number of training times is reached to obtain the trained property prediction model.

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