Electrode material performance simulation calculation method

Abstract

The invention discloses an electrode material performance simulation calculation method, and belongs to the technical field of new energy materials, and the method comprises the following steps: S1, constructing a crystal structure model for an electrode material, and generating a surface reconstruction model according to the crystal structure model; s2, calculating key performance indexes for the electrode material based on the surface reconstruction model; s3, calculating the coordination number of the electrode material; and S4, according to the key performance index and coordination number of the electrode material, carrying out stability evaluation on the electrode material. According to the method, the electrode material can be quickly and efficiently screened, the performance of the electrode material can be comprehensively evaluated, optimal design of the electrode material can be carried out according to actual use conditions through accurate theoretical analysis, resource waste possibly generated in the traditional experiment process is remarkably reduced, and the research and development efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of new energy materials, and in particular relates to a method for simulating and calculating electrode material performance. Background Art

[0002] With the popularity of personal electric vehicles and the application of a large number of mobile electronic devices, people's requirements for battery performance are increasing. Although electrode materials in the current market have made certain progress, from a long-term perspective, current electrode materials still cannot meet the energy density, cycle life and safety requirements of high-performance energy storage systems. Therefore, the development of high-performance electrode materials is the focus of current industry research.

[0003] With the widespread popularity of personal electric vehicles and the widespread application of mobile electronic devices, the demand for battery performance is increasing. Although electrode materials in the existing market have made certain progress, in the long run, existing materials still cannot meet the strict requirements of high-performance energy storage systems in terms of energy density, cycle life and safety. Therefore, the performance simulation of new high-performance electrode materials has become a research hotspot and a key issue to be solved in the current field of battery technology. In response to this demand, the present invention proposes an innovative electrode material performance simulation calculation method, which aims to screen and improve the comprehensive performance of materials to meet the high standards of future energy storage systems. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a method for simulating and calculating electrode material performance.

[0005] The technical solution of the present invention is: a method for simulating and calculating electrode material performance comprises the following steps:

[0006] S1. construct a crystal structure model for the electrode material, and generate a surface reconstruction model based on the crystal structure model;

[0007] S2. Calculate key performance indicators for electrode materials based on the surface reconstruction model;

[0008] S3. Calculate the coordination number of the electrode material;

[0009] S4. Evaluate the stability of electrode materials based on their key performance indicators and coordination numbers.

[0010] Furthermore, S1 includes the following sub-steps:

[0011] S11. Construct a crystal structure model for the electrode material;

[0012] S12, converting the crystal structure model into a surface model;

[0013] S13, presetting a vacuum layer on the surface model, and optimizing the surface model to obtain a surface reconstruction model.

[0014] Furthermore, S2 includes the following sub-steps:

[0015] S21, determining embedded ions for electrode materials;

[0016] S22, placing the embedded ions on top of the surface reconstruction model and performing ab initio molecular dynamics simulation;

[0017] S23. Calculate the theoretical capacity of the electrode material based on the ab initio molecular dynamics simulation results;

[0018] S24. When the theoretical capacity of the electrode material is greater than or equal to the maximum theoretical capacity, calculating the volume expansion rate of the electrode material;

[0019] S25. The theoretical capacity and volume expansion rate of the electrode material are used as key performance indicators.

[0020] Further, S23 includes the following sub-steps:

[0021] S231, extracting a number of frame models when the ab initio molecular dynamics simulation reaches a dynamic equilibrium;

[0022] S232, calculating the theoretical capacity of each frame model, and taking the average of the theoretical capacities of all models as the theoretical capacity of the electrode material.

[0023] Furthermore, in S232, the calculation formula of the theoretical capacity of the model is:

[0024]

[0025] Where n represents the number of embedded ions, m represents the number of atoms of the electrode material, and M represents the molar mass of the electrode material.

[0026] Furthermore, in S24, the calculation formula of the volume expansion rate ΔV of the electrode material is:

[0027]

[0028] Where V 0 represents the volume of the reconstructed model before the ions are embedded, V LiSi is the volume after the reaction.

[0029] Furthermore, in S3, the calculation formula of the coordination number is:

[0030]

[0031] In the formula, r0 represents the bond length when the embedded ion reacts chemically with the electrode material, r ij is the actual distance between the ion and the electrode material, d 0 represents the allowable deviation distance, u represents the first coordination number matching gradient, v represents the second coordination number matching gradient, v>u.

[0032] Furthermore, in S4, MetaDynamics analysis is performed on the electrode material based on the key performance indicators and coordination numbers of the electrode material, and the stability of the analysis results is evaluated.

[0033] Furthermore, the expression for stability evaluation is:

[0034]

[0035] In the formula, k represents the number of cycles, capacity max Indicates the initial maximum capacity, capacity loss Indicates the capacity lost during the cycle.

[0036] The beneficial effects of the present invention are:

[0037] (1) The present invention can quickly and efficiently screen electrode materials and comprehensively evaluate their performance (such as theoretical capacity, etc.). Through precise theoretical analysis, it can optimize the design of electrode materials according to actual usage conditions, significantly reduce the waste of resources that may be generated in traditional experimental processes, and improve research and development efficiency;

[0038] (2) The present invention accurately reproduces the dynamic changes of electrode materials during the charging and discharging process, can effectively promote the modification and screening of electrode materials, and meet the urgent needs of modern mobile electronic devices, electric vehicles, and large-scale energy storage systems for high-performance battery materials. It has broad application prospects and market value. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of the calculation method for simulating the performance of electrode materials;

[0040] Figure 2 This is a schematic diagram of the final model structure;

[0041] Figure 3 This is a schematic diagram of the model after the electrode material is embedded with lithium;

[0042] Figure 4 Schematic diagram of the electrode material model after lithium removal. DETAILED DESCRIPTION

[0043] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0044] like Figure 1 As shown, the present invention provides a method for simulating and calculating electrode material performance, comprising the following steps:

[0045] S1. construct a crystal structure model for the electrode material, and generate a surface reconstruction model based on the crystal structure model;

[0046] S2. Calculate key performance indicators for electrode materials based on the surface reconstruction model;

[0047] S3. Calculate the coordination number of the electrode material;

[0048] S4. Evaluate the stability of electrode materials based on their key performance indicators and coordination numbers.

[0049] The present invention uses the first-principles calculation software CP2K for simulation calculations, combined with MetaDynamics technology, to study the deintercalation reaction behavior of ions in electrode materials by accurately setting different covariates (CVs). This method evaluates the theoretical capacity upper limit of the material by analyzing the open circuit voltage, and judges the cyclic stability of the electrode material by calculating the rate of the deintercalation reaction. Through this comprehensive calculation method, the present invention can efficiently screen out electrode materials with excellent performance, greatly reduce the waste of resources that may be generated in traditional experimental processes, and theoretically provide clear direction and guidance for the research and development of electrode materials. This innovative method not only improves the efficiency of material screening, but also provides a new theoretical basis and technical route for the development of high-performance electrode materials.

[0050] In this embodiment of the present invention, S1 includes the following sub-steps:

[0051] S11. Construct a crystal structure model for the electrode material;

[0052] S12, converting the crystal structure model into a surface model;

[0053] S13, presetting a vacuum layer on the surface model, and optimizing the surface model to obtain a surface reconstruction model.

[0054] In this embodiment of the present invention, S2 includes the following sub-steps:

[0055] S21, determining embedded ions for electrode materials;

[0056] S22, placing the embedded ions on top of the surface reconstruction model and performing ab initio molecular dynamics simulation;

[0057] S23. Calculate the theoretical capacity of the electrode material based on the ab initio molecular dynamics simulation results;

[0058] S24. When the theoretical capacity of the electrode material is greater than or equal to the maximum theoretical capacity, calculating the volume expansion rate of the electrode material;

[0059] S25. The theoretical capacity and volume expansion rate of the electrode material are used as key performance indicators.

[0060] If the electrode material is used in lithium-ion batteries, the embedded ions are lithium ions; if it is used in sodium-ion batteries, the embedded ions are sodium ions. Place the target ions about 100 m above the surface model. The distance between the two is obtained, and an embedded model is constructed on this basis to perform ab initio molecular dynamics (AIMD) simulation. The temperature during the simulation is set slightly higher than room temperature, ranging from 298.15K to 800K, the time step is 0.5-1fs, and the total simulation time is set to 20000fs. If the system reaches equilibrium during the AIMD simulation, the calculation can be terminated in advance; if the equilibrium is not reached within 20000fs, the calculation continues until the system reaches a stable equilibrium. The ensemble selections include NVT and NPT. The heat baths in the molecular dynamics simulation include CSVR, Nose-Hoover, Adaptive-Langevin and Generalized Langevin Equation, and the time constant is set to 200-500. The exchange-correlation functionals include PBE, Pade and B3LYP, and the basis sets are: DZVP-MOLOPT-SR-GTH, TZVP-MOLOPT-GTH, TZV2P-MOLOPT-GTH and TZV2PX-MOLOPT-GTH. The cutoff energy is set to 300-600Ry, the SCF convergence criterion is set between 1E-4 and 1E-8, and the calculation iteration methods include diagonalization method and orbital exchange OT.

[0061] In this embodiment of the present invention, S23 includes the following sub-steps:

[0062] S231, extracting a number of frame models when the ab initio molecular dynamics simulation reaches a dynamic equilibrium;

[0063] S232, calculating the theoretical capacity of each frame model, and taking the average of the theoretical capacities of all models as the theoretical capacity of the electrode material.

[0064] In the embodiment of the present invention, in S232, the calculation formula of the theoretical capacity Capacity of the model is:

[0065]

[0066] Where n represents the number of embedded ions, m represents the number of atoms of the electrode material, and M represents the molar mass of the electrode material.

[0067] 96485 is the Faraday constant (unit: C / mol). Through this formula, the final theoretical capacity unit is mA·h / g. The calculated data of the ten models are statistically averaged to obtain the theoretical capacity of the material. In addition, when the material reaches the maximum theoretical capacity, important parameters such as the volume expansion rate of the electrode material and the charge transfer electronic structure can be further calculated.

[0068] In the embodiment of the present invention, in S24, the calculation formula of the volume expansion rate ΔV of the electrode material is:

[0069]

[0070] Where V 0 represents the volume of the reconstructed model before the ions are embedded, V LiSi is the volume after the reaction.

[0071] In order to evaluate the volume change of the material during lithium insertion, the maximum and minimum coordinate projection values ​​of the electrode material in each direction are first calculated. The volume calculation formula is:

[0072] V=(x max ―x min )×(y max ―y min )×(z max ―z min );

[0073] where x max ,y max 、z max and x min ,y min 、z min Represent the maximum and minimum coordinate projection values ​​of the compound in the XYZ direction, respectively.

[0074] The volume expansion rate reflects the safety of electrode materials. Too high a volume expansion rate may lead to safety hazards in practical applications, so this parameter is of great significance in material screening. This calculation method comprehensively considers key performance indicators such as the capacity and volume expansion of electrode materials, which can provide a scientific basis for the development and optimization of electrode materials and effectively guide the safety and performance improvement of materials.

[0075] In the embodiment of the present invention, in S3, the calculation formula of the coordination number Coordination is:

[0076]

[0077] In the formula, r 0 represents the bond length when the embedded ion reacts chemically with the electrode material, r ijis the actual distance between the ion and the electrode material, d 0 represents the allowable deviation distance, u represents the first coordination number matching gradient, v represents the second coordination number matching gradient, v>u.

[0078] The coordination number ranges from 0 to 1, with a value of 1 indicating the closest proximity between the ion and the electrode material, and a value of 0 indicating the farthest. By setting the Gaussian peak value to 0.1-0.5 times the above energy difference, the width to 10-100, and the width factor ww to 0.01-0.1, AIMD simulation calculations are performed. If the number of ion deintercalation reaches 7-10 times, or the energy fluctuation reaches 7-10 times, the calculation can be judged to be stable and stopped.

[0079] In the embodiment of the present invention, in S4, MetaDynamics analysis is performed on the electrode material according to the key performance indicators and coordination numbers of the electrode material, and the stability of the analysis results is evaluated.

[0080] In an embodiment of the present invention, the expression for stability evaluation is:

[0081]

[0082] In the formula, k represents the number of cycles, capacity max Indicates the initial maximum capacity, capacity loss Indicates the capacity lost during the cycle.

[0083] In an embodiment of the present invention, the cyclic stability of the electrode material can be evaluated by analyzing the diffusion behavior and rate of atoms during the extraction process. Some ions may not be able to completely extract the electrode material during the extraction reaction. This phenomenon reflects the irreversible side reactions or the formation of inactive substances that may occur in the electrode material during the charge and discharge cycle, which in turn leads to the attenuation of the battery capacity after multiple cycles. Therefore, combined with MetaDynamics analysis, the cyclic stability and intrinsic stability of different electrode materials can be effectively evaluated, providing theoretical support for battery performance optimization.

[0084] This method combines a comprehensive analysis of energy, coordination number, and diffusion behavior, which not only enables a deep understanding of the mechanism of ion deintercalation, but also enables accurate prediction of performance degradation issues that electrode materials may face during actual cycling, thus providing an important basis for the design and improvement of high-performance batteries.

[0085] Cycling stability can be estimated by the lost capacity. Since molecular dynamics simulation cannot cover the complete charge and discharge cycle time, the overall performance stability of the electrode material can be evaluated by the capacity decay during multiple cycles.

[0086] This method can effectively predict the stability of electrode materials after multiple cycles by quantifying the relationship between capacity decay and cycle number, providing a scientific basis for material performance optimization and screening. Through this evaluation method, electrode materials with higher stability and longer service life can be screened out at an early stage.

[0087] The present invention innovatively realizes the full process simulation of battery electrochemical cycle at the atomic scale, accurately captures the dynamic changes of ion embedding and de-embedding of electrode materials during the charging and discharging process, breaks through the bottleneck that traditional experimental methods cannot fully observe at the microscopic level, and provides an unprecedented scientific basis for the theoretical optimization of battery performance and the design of new electrode materials.

[0088] The present invention can effectively evaluate the key properties of electrode materials such as cycle stability and volume expansion rate from multiple dimensions, and has significant advantages in the safety research of battery materials. By simulating the volume change of batteries during long-term cycles and the possible formation of inactive substances, it deeply reveals the potential safety hazards of electrode materials under high load conditions, and provides a scientific basis for the safety design of high-performance batteries.

[0089] The present invention innovatively establishes a prediction model for battery capacity decay by systematically and quantitatively analyzing the capacity of electrode materials and their decay process. This model can not only accurately evaluate the capacity retention of batteries after multiple charge and discharge cycles, but also reveal the inherent mechanism of capacity decay, providing practical theoretical guidance for improving battery life and optimizing electrode materials.

[0090] According to different electrode materials, build the crystal structure model of the material, and convert the model into a surface model according to the actual crystal face that is easy to expose. The vacuum layer of the model is optimized on the surface structure.

[0091] The following is an explanation with reference to examples, taking Si electrode material as an example.

[0092] 1. Construction of Si electrode pre-lithium embedding model.

[0093] a. Build a structural model: The crystal structure of Si electrode materials can be built by yourself or downloaded from the material crystallography database. Based on the crystal structure model of Si, referring to relevant literature and research, the most easily exposed crystal face of Si is generally the (100) crystal face. By writing a script for processing, the Si surface model is obtained, and the surface of the model is set The vacuum layer ensures a surface size of approximately Given that the Si surface may be reconstructed, the surface atoms must be further processed. The specific method is to randomly move some surface atoms around the original coordinates, and finally obtain the reconstructed model of the Si surface through surface structure optimization to ensure that its structure is stable and meets the actual application requirements.

[0094] b. Addition and optimization of excess Li atoms: Add excess amorphous Li atoms above the Si surface model and ensure that the distance between Li atoms is greater than or equal to the bond length of metal Li to avoid excessive interaction in the Li region and cause the model to collapse. At the same time, in order to avoid excessive extrusion and the influence of large repulsion on the model, the distance between Li atoms and the Si surface should be kept at The final model is as follows Figure 2 As shown, the upper atoms are Li atoms and the lower atoms are Si atoms.

[0095] 2. Implementation of the lithium insertion process.

[0096] a. Calculation parameter setting: To simulate the lithium insertion process, set the following calculation parameters:

[0097] Some AIMD parameters: Select NVT ensemble and set the temperature to 800K to accelerate the system to reach the reaction equilibrium state. The time step is set to 1fs and the total time is set to 20000fs. The CSVR heat bath is used in the molecular dynamics simulation, and the time constant is set to 200. The exchange correlation functional is PBE, and the basis set is: DZVP-MOLOPT-SR-GTH. The cutoff energy is set to 300Ry, the SCF convergence criterion is set to 1E-5, and the calculation method uses the diagonalization method.

[0098] b. Calculation process monitoring and temperature adjustment: During the calculation process, the energy and temperature fluctuations of the system are continuously monitored. If the system energy and temperature are stable within 1000fs and no longer fluctuate, it can be judged that the calculation has reached a preliminary equilibrium, and the calculation is stopped at this time. Subsequently, the temperature is adjusted to 298.15K to ensure the consistency of the physical properties of the electrode material at room temperature, and the calculation is continued according to the above parameters until the entire system reaches dynamic equilibrium.

[0099] c. Extraction of dynamic equilibrium trajectory and calculation of theoretical capacity: Extract the molecular dynamics trajectory from the dynamic equilibrium stage, divide it into ten models on average, and count the theoretical capacity in each model. The calculation method of theoretical capacity is as follows: First, calculate the number of embedded lithium ions and set the chemical reaction length of Li-Si bond to On this basis, the number of Li atoms that directly reacted with Si was counted, and these Li atoms were regarded as effective ions that contributed to the theoretical capacity. The remaining Li ions that did not react were regarded as invalid and deleted without contributing to the capacity. Finally, the theoretical capacity values ​​calculated by the ten models were averaged to obtain the embedded theoretical capacity of the electrode material, and the calculated result was 4008mAh / g. Figure 3 Schematic diagram of the model after lithium insertion of electrode material.

[0100] Similarly, the LiSi alloying products were screened and the volume expansion rate was calculated: the volume change and volume expansion rate after LiSi alloying were calculated through the coordinate information in three directions. The calculation results show that the volume expansion rate of Si is 342.68%. This result shows that Si has undergone extremely drastic volume expansion during the alloying process, indicating that this material may have safety risks in practical applications. Therefore, it is necessary to consider mixing Si with other materials in practical applications to effectively inhibit its volume expansion and improve the safety and stability of electrode materials.

[0101] 3. Implementation of the lithium removal process.

[0102] a. Calculation parameter setting: To simulate the delithiation process, set the following calculation parameters:

[0103] Some AIMD parameters: Select NVT ensemble and set the temperature to 800K to accelerate the system to reach the reaction equilibrium state. The time step is set to 1fs and the total time is set to 20000fs. The CSVR heat bath is used in the molecular dynamics simulation, and the time constant is set to 200. The exchange correlation functional is PBE, and the basis set is: DZVP-MOLOPT-SR-GTH. The cutoff energy is set to 300Ry, the SCF convergence criterion is set to 1E-5, and the calculation method uses the diagonalization method.

[0104] MetaDynamics settings: Set CV1 to the coordination number of all lithium ions and silicon particles, and set an upper limit wall to prevent lithium ions from escaping the simulation area excessively. CV2 is the energy value, which represents the difference between the initial energy and the energy at dynamic equilibrium during the AIMD calculation process. Set the Gaussian peak to one-tenth of CV2, the Gaussian width to 30, and the width factor ww to 0.02. During the de-lithiation process, when the number of lithium ion de-insertion reaches 7 times and the energy fluctuation reaches 7 repetitions, the calculation can be stopped, indicating that the lithium ions have completed 7 charge and discharge cycles.

[0105] b. Calculation of lost capacity and estimation of theoretical capacity: After delithiation, the molecular dynamics trajectory of the dynamic equilibrium stage is extracted, the trajectory data is evenly divided into ten models, and the theoretical capacity lost in each model is counted. The theoretical capacity calculation method is as follows: Set the Li-Si bond length to The number of lithium ions that have not escaped is counted, and the capacity lost during the cycle is calculated. Finally, the loss capacity values ​​calculated by the ten models are averaged, and the theoretical capacity loss of the electrode material is 683 mAh / g. Figure 4 Schematic diagram of the electrode material model after lithium removal.

[0106] The above simulation results show that the theoretical capacity loss of the electrode material is 683mAh / g after 7 charge and discharge cycles. According to the performance evaluation, the capacity retention rate is 82.9% after 7 cycles. Further calculations show that when the number of cycles reaches 9, the capacity retention rate drops to 78.5%, which indicates that the electrode material has become unstable at this time, and the Si material has a more significant capacity attenuation during the charge and discharge process. Therefore, it is necessary to introduce other materials or modification methods to improve the cycle performance of the electrode material and enhance its stability and durability during long-term use.

[0107] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for simulating and calculating electrode material performance, characterized in that: The following steps are involved: S1. construct a crystal structure model for the electrode material, and generate a surface reconstruction model based on the crystal structure model; S2. Calculate key performance indicators for electrode materials based on the surface reconstruction model; S3. Calculate the coordination number of the electrode material; S4. Evaluate the stability of electrode materials based on their key performance indicators and coordination numbers.

2. The electrode material performance simulation calculation method according to claim 1, characterized in that: The S1 comprises the following sub-steps: S11. Construct a crystal structure model for the electrode material; S12, converting the crystal structure model into a surface model; S13, presetting a vacuum layer on the surface model, and optimizing the surface model to obtain a surface reconstruction model.

3. The electrode material performance simulation calculation method according to claim 1, characterized in that: The S2 comprises the following sub-steps: S21, determining embedded ions for electrode materials; S22, placing the embedded ions on top of the surface reconstruction model and performing ab initio molecular dynamics simulation; S23. Calculate the theoretical capacity of the electrode material based on the ab initio molecular dynamics simulation results; S24. When the theoretical capacity of the electrode material is greater than or equal to the maximum theoretical capacity, calculating the volume expansion rate of the electrode material; S25. The theoretical capacity and volume expansion rate of the electrode material are used as key performance indicators.

4. The electrode material performance simulation calculation method according to claim 3, characterized in that: The S23 comprises the following sub-steps: S231, extracting a number of frame models when the ab initio molecular dynamics simulation reaches a dynamic equilibrium; S232, calculating the theoretical capacity of each frame model, and taking the average of the theoretical capacities of all models as the theoretical capacity of the electrode material.

5. The electrode material performance simulation calculation method according to claim 4, characterized in that: In S232, the calculation formula of the theoretical capacity of the model is: Where n represents the number of embedded ions, m represents the number of atoms of the electrode material, and M represents the molar mass of the electrode material.

6. The electrode material performance simulation calculation method according to claim 3, characterized in that: In S24, the calculation formula of the volume expansion rate ΔV of the electrode material is: Where V0 represents the volume of the surface reconstruction model before ion embedding, V LiSi is the volume after the reaction.

7. The electrode material performance simulation calculation method according to claim 1, characterized in that: In S3, the calculation formula of coordination number Coordination is: In the formula, r0 represents the bond length when the embedded ion reacts chemically with the electrode material, r ij is the actual distance between the ion and the electrode material, d0 represents the allowable deviation distance, u represents the first coordination number matching gradient, and v represents the second coordination number matching gradient.

8. The electrode material performance simulation calculation method according to claim 1, characterized in that: In S4, a MetaDynamics analysis is performed on the electrode material according to the key performance indicators and coordination numbers of the electrode material, and a stability assessment is performed on the analysis results.

9. The electrode material performance simulation calculation method according to claim 8, characterized in that: The expression for the stability evaluation is: In the formula, k represents the number of cycles, capacity max Indicates the initial maximum capacity, capacity loss Indicates the capacity lost during the cycle.

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