Method for analyzing decomposition process of Mg ion battery electrolyte based on reactive molecular dynamics simulation

Abstract

The invention relates to a method for analyzing an electrolyte decomposition process of an Mg ion battery based on reactive molecular dynamics simulation, and the method comprises the steps: constructing an Mg ion battery model, carrying out reactive force field molecular dynamics simulation, simulating an external voltage condition of the battery model by using an electrochemical dynamics method, and simulating charge transfer between ions by using a charge balance method. The three methods are combined to simulate the discharge process of the Mg ion battery, and then a reaction network integrator is adopted to track the species of all atoms in the electrolyte of the Mg ion battery in each snapshot, so that the decomposition process of the electrolyte is simulated. Compared with the prior art, the method has the advantages that an electrolyte decomposition path can be analyzed more accurately and organic, inorganic and gas products can be identified by utilizing a graph theory-based ReacNetGenerator mode. The provided multi-element mixed analysis simulation means can simulate the reaction process more truly, the electrolyte decomposition process can be analyzed from the perspective of a micromechanism, and a basis is provided for further design of the Mg battery electrolyte.

Description

Technical Field

[0001] The present invention relates to the technical field of electrochemical simulation, and in particular to a method for analyzing the decomposition process of a Mg ion battery electrolyte based on reactive molecular dynamics simulation. Background Art

[0002] Mg-ion batteries with higher theoretical capacity and better safety are energy storage devices with excellent development potential. However, the poor stability of related battery electrolytes has hindered further research on Mg-ion batteries. In order to improve battery performance, the decomposition of Mg-ion battery electrolytes has been studied by various methods such as SEM, cryo-electron microscopy, XPS, and gas analysis. However, these experimental analysis methods cannot study the specific microscopic process of electrolyte decomposition.

[0003] In order to make up for the shortcomings of experimental methods, various theoretical calculation methods, such as small-scale calculations such as first principles (DFT) and ab initio molecular dynamics (AIMD) or large-scale calculations such as classical dynamics (MD) and reaction force field molecular dynamics (Reax-ff MD) are used to study the microscopic mechanism of the reaction process. However, DFT and AIMD have high computational costs and are limited to small-scale (hundreds of atoms) and short-time (fs level) simulations. Classical MD cannot be used to simulate the breaking and bonding processes of chemical bonds. The single ReaxFF-MD is slightly insufficient in predicting and analyzing the various products in the electrolyte reaction process as comprehensively as possible.

[0004] Therefore, it is urgent to develop a method that can effectively simulate the internal microscopic processes of Mg-ion battery electrolytes. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation. By using the ReacNetGenerator method based on graph theory, the electrolyte decomposition path can be analyzed more accurately, and organic, inorganic and gas products can be identified more accurately.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides a method for analyzing the decomposition process of an electrolyte of an Mg ion battery based on reactive molecular dynamics simulation. The method comprises: constructing an Mg ion battery model, using reaction force field molecular dynamics to simulate the bonding and breaking of molecules, using an implicit degree of freedom electrochemical kinetics method to simulate external voltage, and using a charge balance method to simulate the charge transfer between ions, combining the three simulation methods to simulate the discharge process of the Mg ion battery, and then using a reaction network integrator to track the species in each snapshot of all atoms in the electrolyte of the Mg ion battery to obtain material conversion information, thereby simulating the decomposition process of the electrolyte.

[0008] Furthermore, the Mg ion battery model includes a cathode, an anode and an electrolyte, the cathode and the anode are both periodically repeated magnesium metal units, and the electrolyte includes a magnesium salt and an organic solvent.

[0009] Furthermore, the organic solvent is one or more of DME, DGM, and THF, and the magnesium salt is Mg(TFSI)2.

[0010] Furthermore, the electrolyte components constructed in the Mg-ion battery model include 0.5M Mg(TFSI)2 dissolved in DME, 0.5M Mg(TFSI)2 dissolved in DGM, and 0.5M Mg(TFSI)2 and 2M DMA dissolved in THF.

[0011] Furthermore, the reaction force field molecular dynamics is a reaction force field potential function molecular dynamics, which describes the bonding relationship between atoms through parameters such as interatomic distance and atomic charge, and then simulates the bonding and breaking of molecules.

[0012] Furthermore, the electrochemical kinetics is an implicit degree of freedom electrochemical kinetics, which assigns additional variables to each atom in the model to represent nearby local points so as to apply an external voltage to the Mg ion battery model.

[0013] Furthermore, the charge balance is used to calculate the changes in atomic charges during the simulation process.

[0014] Furthermore, the specific process of acquiring each snapshot of all atoms in the electrolyte of the Mg-ion battery is as follows: Lammps software is used to record an atomic trajectory snapshot every 0.2 ps.

[0015] Furthermore, the reaction network integrator adopts a reaction network integrator based on graph theory.

[0016] Furthermore, a graph-theory-based reaction network integrator was used to read the trajectory files containing bond order information in the reactive molecular dynamics simulation, and each snapshot in the trajectory file was treated as an undirected graph, with each atom regarded as a node;

[0017] The species to which each node belongs are traversed and identified to obtain all species on a given trajectory. The Markov model is then used to filter the trajectory noise to obtain the reaction network and product information.

[0018] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0019] 1. The present invention uses the ReacNetGenerator method based on graph theory to more accurately analyze the electrolyte decomposition path and more accurately identify organic, inorganic and gas products.

[0020] 2. The multivariate mixed analysis simulation method proposed in the present invention can simulate the reaction process more realistically, and can analyze the electrolyte decomposition process from the perspective of microscopic mechanism, providing a basis for the further design of Mg battery electrolytes. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Flow chart of a method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation. DETAILED DESCRIPTION

[0022] The specific implementation methods of the present invention are described in detail below through examples. These examples are implemented on the premise of the scheme described in the present invention, and provide detailed implementation methods and specific operating processes, but the protection scope of the present invention is not limited to the following examples.

[0023] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments. Any features such as component models, material names, connection structures, preparation methods, materials, structures or composition ratios not clearly described in this technical solution are deemed to be common technical features disclosed in the prior art.

[0024] Example 1

[0025] This embodiment provides a method for analyzing the decomposition process of Mg ion battery electrolyte based on reactive molecular dynamics simulation. Figure 1 As shown, the method includes:

[0026] S1: Construct a Mg ion battery model, which includes a cathode, an anode and an electrolyte. The cathode and the anode are both periodically repeated magnesium metal units, and the electrolyte is 0.5M magnesium salt Mg(TFSI)2 dissolved in ethylene glycol dimethyl ether (DME).

[0027] S2: Reaction force field molecular dynamics (ReaxFF-MD) is used to simulate the bonding and breaking of molecules, implicit degree of freedom electrochemical kinetics (EChemDID) is used to simulate external voltage, and charge balance method (QEq) is used to simulate the charge transfer between ions. The three simulation methods are combined to simulate the discharge process of Mg ion battery. Reaction force field potential function molecular dynamics describes the bonding relationship between atoms through parameters such as interatomic distance and atomic charge, and then simulates the bonding and breaking of molecules during the simulation process. Implicit degree of freedom electrochemical kinetics assigns additional variables to each atom in the model to represent nearby local points to apply external voltage to the Mg ion battery model. The charge balance is used to calculate the changes in atomic charge during the simulation process. It is a method for predicting the charge distribution in molecules. The atomic chemical potential is constructed by using atomic ionization potential, electronegativity, atomic radius, and shielding electrostatic interactions between all charges. By requiring equal chemical potential, a balanced charge that depends on the geometry can be obtained.

[0028] S3: A graph-based reaction network integrator is used to track the species in each snapshot of all atoms in the electrolyte of the Mg-ion battery, obtain material transformation information, and simulate the decomposition process of the electrolyte. The specific acquisition process of each snapshot of all atoms in the electrolyte of the Mg-ion battery is to use Lammps software to record an atomic trajectory snapshot every 0.2ps.

[0029] A graph-theory-based reaction network integrator is used to read trajectory files containing bond order information in reactive molecular dynamics simulations, and each snapshot in the trajectory file is treated as an undirected graph with each atom as a node. The species to which each node belongs are traversed and identified to obtain all species on a given trajectory. The Markov model is then used to filter trajectory noise to obtain reaction network and product information.

[0030] Example 2

[0031] The difference between this embodiment and embodiment 1 is that the electrolyte is 0.5 M magnesium salt Mg(TFSI)2 dissolved in diethylene glycol dimethyl ether (DGM), and the remaining steps are the same as those in embodiment 1.

[0032] Example 3

[0033] The difference between this embodiment and embodiment 1 is that the electrolyte is 0.5M magnesium salt Mg(TFSI)2 and 2M dimethylacetamide (DMA) dissolved in tetrahydrofuran (THF), and the remaining steps are the same as those in embodiment 1.

[0034] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation, characterized in that: The method comprises: constructing a Mg ion battery model, using reaction force field molecular dynamics to simulate the bonding and breaking of molecules, using implicit degree of freedom electrochemical kinetics to simulate external voltage, and using a charge balance method to simulate the charge transfer between ions, combining the three simulation methods to simulate the discharge process of the Mg ion battery, and then using a reaction network integrator to track the species in each snapshot of all atoms in the electrolyte of the Mg ion battery to obtain material transformation information, so as to simulate the decomposition process of the electrolyte.

2. The method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation according to claim 1, characterized in that: The Mg ion battery model includes a cathode, an anode and an electrolyte, wherein the cathode and the anode are both periodically repeated magnesium metal units, and the electrolyte includes a magnesium salt and an organic solvent.

3. The method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation according to claim 2, characterized in that: The organic solvent is one or more of DME, DGM, and THF, and the magnesium salt is Mg(TFSI)2.

4. The method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation according to claim 2, characterized in that: The electrolyte components constructed in the Mg-ion battery model include 0.5M Mg(TFSI)2 dissolved in DME, 0.5M Mg(TFSI)2 dissolved in DGM, and 0.5M Mg(TFSI)2 and 2M DMA dissolved in THF.

5. The method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation according to claim 1, characterized in that: The reaction force field molecular dynamics is a reaction force field potential function molecular dynamics, which describes the bonding relationship between atoms through parameters such as interatomic distance and atomic charge, and then simulates the bonding and breaking of molecules.

6. The method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation according to claim 1, characterized in that: The electrochemical kinetics is an implicit degree of freedom electrochemical kinetics, which assigns additional variables to each atom in the model to represent nearby local points so as to apply an external voltage to the Mg ion battery model.

7. The method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation according to claim 1, characterized in that: The charge balance is used to calculate the changes in atomic charge that occur during the simulation.

8. The method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation according to claim 1, characterized in that: The specific process of acquiring each snapshot of all atoms in the electrolyte of the Mg-ion battery is as follows: Lammps software is used to record an atomic trajectory snapshot every 0.2 ps.

9. The method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation according to claim 1, characterized in that: The reaction network integrator adopts a reaction network integrator based on graph theory.

10. The method for analyzing the decomposition process of Mg-ion battery electrolyte based on reactive molecular dynamics simulation according to claim 9, characterized in that: A graph-based reaction network integrator is used to read the trajectory file containing bond order information in the reactive molecular dynamics simulation, and each snapshot in the trajectory file is regarded as an undirected graph, with each atom as a node. The species to which each node belongs are traversed and identified to obtain all species on a given trajectory. The Markov model is then used to filter the trajectory noise to obtain the reaction network and product information.

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