Simulation method for interface stability of molecular-level surface-modified carbon negative electrode materials

Abstract

The present application relates to the technical field of digital simulation, and particularly relates to a kind of interface stability simulation method of molecular level surface modification carbon negative electrode material.The method comprises the following steps: obtaining carbon negative electrode material structure data and molecular level surface modification design parameters;Design molecular level surface modification carbon negative electrode model based on carbon negative electrode material structure data and molecular level surface modification design parameters;Analysis of the interface multidimensional attribute feature data of molecular level surface modification carbon negative electrode model and ion transport interface degradation feature data;Based on interface multidimensional attribute dynamic feature data and ion transport interface degradation feature data, stability evaluation processing is carried out for interface simulation, to obtain interface simulation stability evaluation data;Interface simulation stability evaluation data is transmitted to terminal to carry out interface stability simulation feedback operation of molecular level surface modification carbon negative electrode material.The present application realizes the accurate analysis of the interface stability of molecular level surface modification carbon negative electrode material efficiently and simulates.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital simulation, in particular to an interface stability simulation method for a molecular-level surface modified carbon negative electrode material. BACKGROUND

[0002] In recent years, with the development of renewable energy and electric vehicles, batteries are widely used due to their high energy density, long cycle life and good safety. Among the negative electrode materials of the battery, carbon-based materials are still the main choice, and their electrochemical performance, cycle stability and interface stability directly affect the overall performance and service life of the battery. During the long-term cycle process of traditional carbon negative electrode materials, the interface stability is often abnormal, which leads to accelerated capacity attenuation and reduced service life of the battery. In order to improve the interface stability of the carbon negative electrode material, by introducing functional molecules or functional groups on the surface of the carbon negative electrode, the interface chemical environment can be adjusted, the ion transmission channel can be optimized, and the surface electronic structure stability can be improved. However, the existing interface stability simulation technology for molecular-level surface modified carbon negative electrode materials mainly relies on macroscopic electrochemical tests to evaluate the effect of molecular modification, and it is difficult to obtain the microcosmic multi-dimensional attribute change information of the carbon negative electrode interface; on the other hand, it is difficult to accurately analyze the changes in interface structure characteristics and ion transmission behavior after molecular modification, resulting in poor simulation analysis effect of the interface stability of the molecular-level surface modified carbon negative electrode material. SUMMARY

[0003] Therefore, the present application provides an interface stability simulation method for a molecular-level surface modified carbon negative electrode material to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, an interface stability simulation method for a molecular-level surface modified carbon negative electrode material comprises the following steps:

[0005] Step S1: Obtain carbon negative electrode material structure data and molecular-level surface modification design parameters; perform molecular-level surface modification modeling processing on the carbon negative electrode based on the carbon negative electrode material structure data and the molecular-level surface modification design parameters to obtain a molecular-level surface modified carbon negative electrode model;

[0006] Step S2: Perform interface group analysis on the molecular-level surface modified carbon negative electrode model to generate interface group data; perform interface multi-dimensional attribute feature analysis on the molecular-level surface modified carbon negative electrode based on the interface group data to generate interface multi-dimensional attribute feature data;

[0007] Step S3: Perform ion dynamic trajectory integrated analysis processing on the molecular-level surface modified carbon negative electrode model through a preset battery system embedding parameter to generate integrated ion dynamic response trajectory data;

[0008] Step S4: Perform dynamic change analysis of the interface multi-dimensional attribute characteristics based on the integrated ion dynamic response trajectory data, to generate interface multi-dimensional attribute dynamic characteristic data; perform interface degradation characteristic analysis of ion transmission based on the interface multi-dimensional attribute dynamic characteristic data and the integrated ion dynamic response trajectory data, to generate ion transmission interface degradation characteristic data;

[0009] Step S5: Perform stability evaluation processing of the interface simulation based on the interface multi-dimensional attribute dynamic characteristic data and the ion transmission interface degradation characteristic data, to obtain interface simulation stability evaluation data; transmit the interface simulation stability evaluation data to the terminal to perform feedback work of the interface stability simulation of the molecular-level surface modified carbon negative electrode material.

[0010] Further, the carbon negative electrode material structure data in step S1 includes carbon negative electrode crystal structure type data, carbon negative electrode atomic characteristic data, carbon negative electrode pore distribution data, and carbon negative electrode defect point coordinate data, and the molecular-level surface modification design parameters include modification molecule chemical structure, modification molecule functional group type data, and modification molecule bonding mode data.

[0011] Further, step S1 includes the following steps:

[0012] Step S11: Obtain carbon negative electrode material structure data and molecular-level surface modification design parameters;

[0013] Step S12: Extract carbon negative electrode structure coordinate data according to the carbon negative electrode material structure data, and perform index identification of the carbon negative electrode structure coordinate data to obtain carbon negative electrode structure coordinate index data;

[0014] Step S13: Perform carbon negative electrode structure modeling on the carbon negative electrode material structure data using the carbon negative electrode structure coordinate index data, to obtain a carbon negative electrode structure model;

[0015] Step S14: Perform molecular surface modification space grid attribute analysis based on the carbon negative electrode structure model, to obtain molecular surface modification space grid attribute data;

[0016] Step S15: Perform spatial docking geometry analysis processing of the molecular-level surface modification based on the molecular-level surface modification design parameters and the molecular surface modification space grid attribute data, to generate molecular-level surface modification spatial docking data;

[0017] Step S16: Perform carbon negative electrode modeling processing of the molecular-level surface modification using the molecular-level surface modification spatial docking data and the carbon negative electrode structure model, to obtain a molecular-level surface modified carbon negative electrode model.

[0018] Further, step S14 includes the following steps:

[0019] Step S141: performing carbon negative electrode structure space specificity analysis according to the carbon negative electrode structure model to obtain carbon negative electrode space specificity data, wherein the carbon negative electrode space surface curvature data, the carbon negative electrode surface exposure degree data, and the carbon negative electrode local electron affinity approximation data;

[0020] Step S142: performing spatial grid division processing of molecule surface modification specificity according to the carbon negative electrode space specificity data to obtain molecule surface modification specificity spatial grid data;

[0021] Step S143: performing molecule surface modification spatial grid attribute identification on the molecule surface modification specificity spatial grid data to obtain molecule surface modification spatial grid attribute data.

[0022] Further, step S15 includes the following steps:

[0023] Step S151: performing candidate docking evaluation of molecule level surface modification on the molecule surface modification spatial grid attribute data according to the molecule level surface modification design parameter to generate molecule level surface modification candidate docking evaluation data;

[0024] Step S152: performing candidate docking local conformation enumeration simulation processing according to the molecule level surface modification candidate docking evaluation data to obtain molecule level surface modification finite set conformation data;

[0025] Step S153: performing spatial docking geometry conflict optimization of the molecule level surface modification through the molecule level surface modification finite set conformation data to generate molecule level surface modification spatial docking data.

[0026] Further, step S2 includes the following steps:

[0027] Step S21: performing interface group analysis of the molecule level surface modification carbon negative electrode model with the carbon negative electrode to generate interface group data;

[0028] Step S22: performing interface group force field type analysis processing according to the interface group data to generate interface group force field type data;

[0029] Step S23: performing interface bond length, bond angle, and dihedral angle parameter analysis processing according to the interface group data to generate interface bonding interaction data;

[0030] Step S24: extracting molecule level surface charge parameters and carbon negative electrode charge parameters through the molecule level surface modification carbon negative electrode model, and performing interface charge transport feature analysis on the interface group data according to the molecule level surface charge parameters and the carbon negative electrode charge parameters to generate interface charge transport feature data;

[0031] Step S25: performing interface non-bond potential analysis on the interface bonding interaction data and the interface charge transport characteristic data according to a preset non-bond potential cutoff distance parameter to obtain interface non-bond potential data, and performing non-bond potential scaling adjustment processing on the interface non-bond potential data by using a preset polarization effect scaling factor to obtain interface scaled non-bond potential data;

[0032] Step S26: performing interface multi-dimensional attribute characteristic analysis of the molecular-level surface-modified carbon negative electrode according to the interface group force field type data, the interface bonding interaction data, the interface charge transport characteristic data, and the interface optimized non-bond potential data to generate interface multi-dimensional attribute characteristic data.

[0033] Further, step S3 includes the following steps:

[0034] Step S31: performing carbon negative electrode multi-scenario dynamic response simulation processing on the molecular-level surface-modified carbon negative electrode model according to a preset battery system intercalation parameter to generate carbon negative electrode multi-scenario dynamic response simulation data;

[0035] Step S32: performing multi-scenario ion dynamic response trajectory analysis according to the carbon negative electrode multi-scenario dynamic response simulation data to generate multi-scenario ion dynamic response trajectory data;

[0036] Step S33: performing trajectory integration processing on the multi-scenario ion dynamic response trajectory data to obtain integrated ion dynamic response trajectory data.

[0037] Further, step S4 includes the following steps:

[0038] Step S41: performing interface multi-dimensional attribute characteristic dynamic change analysis on the interface multi-dimensional attribute characteristic data according to the integrated ion dynamic response trajectory data to generate interface multi-dimensional attribute dynamic characteristic data;

[0039] Step S42: performing ion embedding and deposition distribution analysis on the interface according to the integrated ion dynamic response trajectory data to obtain interface ion embedding and deposition distribution data;

[0040] Step S43: performing deposition distribution gradient characteristic analysis according to the interface ion embedding and deposition distribution data to obtain interface ion embedding and deposition distribution gradient characteristic data, and designing interface ion embedding and deposition energy field data through the interface ion embedding and deposition distribution gradient characteristic data;

[0041] Step S44: performing ion transport interface potential barrier characteristic analysis on the integrated ion dynamic response trajectory data according to the interface ion embedding and deposition energy field data to generate ion transport interface potential barrier characteristic data;

[0042] Step S45: Perform free energy characteristic analysis of ion transmission at the interface according to the interface multi-dimensional attribute dynamic characteristic data and the ion transmission interface potential barrier characteristic data to obtain ion transmission interface free energy characteristic data;

[0043] Step S46: Perform interface degradation characteristic analysis of ion transmission according to the ion transmission interface potential barrier characteristic data and the ion transmission interface free energy characteristic data to generate ion transmission interface degradation characteristic data.

[0044] Further, step S46 includes the following steps:

[0045] Perform interface degradation influencing factor analysis of ion transmission according to the ion transmission interface potential barrier characteristic data and the ion transmission interface free energy characteristic data to obtain ion transmission interface degradation influencing factor data, and perform interface degradation energy spectrum clustering processing of ion transmission on the ion transmission interface potential barrier characteristic data and the ion transmission interface free energy characteristic data by taking the ion transmission interface degradation influencing factor data as the interface degradation clustering correlation parameter to obtain ion transmission interface degradation energy spectrum clustering data;

[0046] Perform interface degradation characteristic analysis of ion transmission according to the ion transmission interface degradation energy spectrum clustering data to generate ion transmission interface degradation characteristic data.

[0047] Further, step S5 includes the following steps:

[0048] Step S51: Perform interface simulation double-branch stability correlation characteristic analysis on the interface multi-dimensional attribute dynamic characteristic data and the ion transmission interface degradation characteristic data by using a preset double-branch neural network algorithm to generate interface simulation double-branch stability correlation characteristic data; perform stability correlation characteristic high-dimensional feature splicing processing on the interface simulation double-branch stability correlation characteristic data by using a channel attention mechanism built in the double-branch neural network algorithm to generate interface simulation stability correlation characteristic splicing data; and perform stability correlation coupling characteristic analysis of interface simulation according to the interface simulation stability correlation characteristic splicing data to generate interface simulation stability correlation coupling characteristic data.

[0049] Step S52: Perform interface simulation stability evaluation processing on the interface simulation stability correlation coupling characteristic data by using a preset interface stability evaluation index to obtain interface simulation stability evaluation data.

[0050] Step S53: Transmit the interface simulation stability evaluation data to a terminal to perform a molecular-level surface modification carbon negative electrode material interface stability simulation feedback job.

[0051] The application has the beneficial effects that the application can accurately reproduce the microstructure characteristics and surface modification of the carbon negative electrode material at the molecular scale by obtaining the carbon negative electrode material structure data and molecular level surface modification design parameters, and performing modeling processing of the molecular level surface modification of the carbon negative electrode based on the data. Further through the carbon negative electrode structure coordinate indexing, spatial grid attribute analysis and spatial docking processing of the molecular modification, the spatial position and docking method of the modified molecules can be accurately determined, and the local electronic environment, curvature and exposure of the carbon negative electrode surface can be quantitatively analyzed. The molecular level fine modeling method significantly improves the visualization and controllability of the carbon negative electrode surface modification scheme, provides reliable basic data for subsequent interface multi-dimensional feature analysis and ion dynamics simulation, and is beneficial to realize efficient optimization and targeted improvement of the modification design. Through interface group analysis, force field type analysis and bond length, bond angle, dihedral angle and other bonding interaction analysis of the molecular level surface modified carbon negative electrode model, combined with charge transport characteristics and non-bond potential optimization processing, the interface multi-dimensional attribute characteristics of the molecular modified carbon negative electrode can be comprehensively described. The influence of different factors on the stability and ion transmission can be quantified, and the interface chemical environment, electronic distribution and non-bond interaction can be accurately evaluated. The multi-dimensional interface analysis method improves the prediction accuracy of the surface modification effect, helps to screen efficient modification molecules and optimize the interface performance of the carbon negative electrode, thereby improving the battery cycle stability and ion transmission efficiency. Through multi-scenario dynamic response simulation based on the preset battery system embedding parameters, the dynamic response trajectories of ions at the interface and inside the carbon negative electrode are generated, and the multi-scenario trajectories are integrated and analyzed, which can comprehensively describe the migration, embedding and interface response behavior of ions in time and space scales. Different modification schemes can be simulated under actual working conditions to reveal the influence of different modification schemes on ion dynamics, and provide dynamic basic data for interface degradation characteristics and stability analysis. This not only improves the authenticity and reliability of the simulation, but also provides a scientific basis for the molecular level optimization design of the carbon negative electrode material, and improves the predictability and applicability of the modification scheme in actual battery applications. Through dynamic change analysis of the interface multi-dimensional attribute characteristics based on the integrated ion dynamic response trajectory data, the microstructure evolution and multi-dimensional attribute change of the carbon negative electrode interface under actual working conditions can be accurately captured. This process further combines the ion embedding deposition distribution, deposition distribution gradient characteristics and corresponding energy field analysis at the interface to reveal the spatial distribution characteristics of the interface potential barrier and free energy in the ion transmission process, and provides a quantitative description of the interface degradation behavior. Through interface degradation influencing factor analysis and energy spectrum clustering processing, the key factors leading to interface failure or performance degradation can be systematically identified and quantified, and high-precision and multi-dimensional evaluation of the ion transmission interface degradation characteristics can be realized. This not only improves the scientificity of the interface stability simulation, but also provides dynamic and predictable data support for the molecular level optimization design of the carbon negative electrode material.The interface multi-dimensional attribute dynamic characteristic data and ion transmission interface degradation characteristic data are analyzed by using a double-branch neural network algorithm, and through a channel attention mechanism and high-dimensional feature splicing, coupling analysis and high-dimensional representation of the interface stability characteristics are realized. The complex interface dynamic behavior and degradation characteristics can be comprehensively evaluated to generate quantitative interface simulation stability evaluation data. The evaluation results are fed back to the terminal execution of the molecular-level surface modified carbon negative electrode material interface stability simulation, which can realize real-time optimization and closed-loop control of the modification scheme. The accuracy, reliability and operability of the interface stability simulation are significantly improved, which provides scientific basis and engineering guidance value for carbon negative electrode material design, and reduces the experimental verification cost.

[0052] Therefore, the interface stability simulation method of the molecular-level surface modified carbon negative electrode material of the present application effectively solves the problem that the existing technology relies on macroscopic electrochemical testing and is difficult to obtain interface microcosmic multi-dimensional attribute information by establishing a fine modeling and interface analysis method of the molecular-level surface modified carbon negative electrode material. The structure characteristics, electron distribution, bonding and non-bonding interaction characteristics of the carbon negative electrode interface are comprehensively described at the molecular scale, and combined with multi-scene ion dynamic response trajectory analysis, the ion transmission behavior and interface evolution process in the interface and the carbon negative electrode are accurately simulated. In addition, the interface degradation characteristic analysis and high-dimensional stability correlation evaluation of the double-branch neural network are used to realize the quantitative and predictable analysis of the molecular modification effect. The precision and reliability of the interface stability simulation are significantly improved, so that the interface optimization design of the molecular-level surface modified carbon negative electrode material can be efficiently screened and optimized based on scientific data, thereby improving the cycle performance of the carbon negative electrode material and the overall performance of the battery, and providing a highly operable guidance method for the engineering application of the molecular modification scheme. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The step flowchart of the interface stability simulation method of the molecular-level surface modified carbon negative electrode material of the present application is shown in the figure;

[0054] Figure 2 The detailed implementation step flowchart of step S2 in the present application is shown in the figure; Figure 1

[0055] Figure 3 The detailed implementation step flowchart of step S4 in the present application is shown in the figure; Figure 1

[0056] The implementation of the present application, functional characteristics and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0057] ​​The technical method of the present application will be described clearly and completely in combination with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0058] In addition, the drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated description thereof will be omitted. Some block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0059] To achieve the above-mentioned object, please refer to Figures 1 to 3 The present application provides a method for simulating the interface stability of a molecular-level surface-modified carbon negative electrode material. In the embodiments of the present application, please refer to Figure 1 Fig. 1 shows a schematic diagram of the step flow of a method for simulating the interface stability of a molecular-level surface-modified carbon negative electrode material according to the present application. The method for simulating the interface stability of a molecular-level surface-modified carbon negative electrode material includes the following steps:

[0060] To achieve the above-mentioned object, a method for simulating the interface stability of a molecular-level surface-modified carbon negative electrode material includes the following steps:

[0061] Step S1: Obtain carbon negative electrode material structure data and molecular-level surface modification design parameters; perform carbon negative electrode modeling processing for molecular-level surface modification based on the carbon negative electrode material structure data and the molecular-level surface modification design parameters to obtain a molecular-level surface-modified carbon negative electrode model;

[0062] In the embodiment of the present application, the structural data of the carbon negative electrode material is obtained, including the crystal structure type, atomic arrangement characteristics, pore distribution and defect point coordinate information of the carbon negative electrode. The spatial coordinate position of each carbon atom is extracted through the carbon negative electrode structure coordinate data, and the carbon negative electrode structure is indexed and identified, so as to establish a structure index system and provide accurate coordinate reference for subsequent surface modification modeling. On this basis, a three-dimensional structure model of the carbon negative electrode is constructed, ensuring that the atomic positions and pore distribution in the model are highly consistent with the actual material structure. According to the molecular-level surface modification design parameters, including the chemical structure, functional group type and bonding mode of the modification molecule, spatial grid analysis of the molecular-level surface modification is performed, the curvature, exposure and local electronic affinity characteristics of different regions on the surface of the carbon negative electrode are analyzed, and these information is converted into operable spatial grid attributes. Combined with the spatial grid attributes of the surface modification molecule, spatial docking geometry analysis is performed, the local conformation set that the modification molecule can form on the surface of the carbon negative electrode is identified, spatial conflicts are eliminated through conformation enumeration method, and a molecular-level surface modification carbon negative electrode model is generated. The model completely reflects the atomic structure on the surface of the carbon negative electrode, the position and conformation of the surface modification molecule, and ensures the basic accuracy of subsequent interface multi-dimensional attribute analysis and ion dynamic simulation.

[0063] Step S2: performing interface group analysis on the molecular-level surface modification carbon negative electrode model to generate interface group data; and performing interface multi-dimensional attribute feature analysis on the molecular-level surface modification carbon negative electrode based on the interface group data to generate interface multi-dimensional attribute feature data.

[0064] In the embodiment of the present application, interface group analysis is performed based on the molecular-level surface modification carbon negative electrode model. The interatomic interaction of the contact region between the surface of the carbon negative electrode and the modification molecule is identified and classified, the types and spatial distribution information of the interface groups are extracted, and interface group data is generated. Force field type analysis is performed on the interface groups, the groups and their corresponding bond energy parameters, angle and dihedral angle parameters are associated, and complete interface bonding interaction data is formed. At the same time, the charge distribution parameters of the modification molecule and the carbon negative electrode atoms are extracted, the interface charge transport characteristics are calculated through charge distribution analysis, and the interface electronic migration and local polarization effect are quantified. Based on the non-potential cutoff distance parameter, the interface non-potential, including van der Waals force and electrostatic interaction, is calculated, and the non-potential is scaled and adjusted through the polarization effect factor to generate interface optimized non-potential data. The interface group force field type, bonding interaction, charge transport characteristics and non-potential data are integrated and analyzed to form complete interface multi-dimensional attribute feature data, which is used to describe the microstructure, mechanics and electrochemical characteristics of the interface of the carbon negative electrode after molecular modification, and realize all-round quantification of the interface characteristics.

[0065] Step S3: Integrated analysis and processing of ion dynamic trajectory sets of the carbon negative electrode model of the molecular level surface modified carbon negative electrode in multi-scenario simulation running of the carbon negative electrode by preset battery system embedding parameters, to generate integrated ion dynamic response trajectory data;

[0066] In the embodiment of the application, the molecular level surface modified carbon negative electrode model and the multi-dimensional attribute feature data of the interface are used to perform multi-scenario dynamic response simulation according to preset battery system embedding parameters. The carbon negative electrode model is placed under different electric fields, electrochemical environments and temperature conditions, and the motion trajectory of ions on the surface and inside the carbon negative electrode is calculated by a molecular dynamics method to obtain the position, speed and force information of the ions under each scenario. The multi-scenario ion motion trajectory is analyzed step by step, and the local motion behavior of the ions under each scenario is unified into an integrated trajectory framework by time integration and trajectory mapping methods to form integrated ion dynamic response trajectory data. The integrated trajectory data completely reflects the migration law, embedding and deposition behavior and interface interaction characteristics of the ions under different working conditions, provides a high-precision quantitative basis for subsequent interface dynamic analysis, barrier and free energy calculation and interface stability evaluation, and enables the interface simulation of the molecular level surface modified carbon negative electrode material to cover the ion response behavior under various actual working environments.

[0067] Step S4: Dynamic change analysis of the multi-dimensional attribute feature data of the interface based on the integrated ion dynamic response trajectory data to generate multi-dimensional attribute dynamic feature data of the interface; interface degradation feature analysis of ion transmission based on the multi-dimensional attribute dynamic feature data of the interface and the integrated ion dynamic response trajectory data to generate ion transmission interface degradation feature data;

[0068] In this embodiment of the invention, integrated ion dynamic response trajectory data is used to perform dynamic change analysis on the multidimensional property feature data of the interface. By analyzing the embedding, diffusion, and migration behavior of ions at different positions on the interface, the conformational changes, bond length fluctuations, bond angle changes, and local electron density distribution changes of interface atoms or groups are coupled with the ion motion trajectory to form dynamic feature data of multidimensional interface properties. Subsequently, based on the ion distribution information in the integrated trajectory, the embedding and deposition distribution of ions in different regions of the interface is finely analyzed, the ion deposition density gradient in each region is calculated, and the gradient change is used to generate the gradient feature of the interface ion embedding and deposition distribution. Furthermore, an interface ion embedding and deposition energy field is constructed to quantify the interface energy changes during ion embedding or migration. This energy field is used to perform barrier analysis on the integrated ion dynamic response trajectory to obtain the local energy barrier data of ion transport on the interface. Then, combined with the dynamic feature data of multidimensional interface properties, free energy feature analysis is performed to quantify the interface energy changes and free energy distribution during ion migration. By combining barrier characteristics and free energy characteristics, interface degradation analysis methods are used to identify interface structural damage and stability changes that may be caused by ion transport, generating interface degradation characteristic data for ion transport, and providing a high-precision microscopic characterization basis for subsequent stability assessment.

[0069] Step S5: Based on the dynamic characteristic data of multidimensional interface properties and the characteristic data of ion transport interface degradation, perform stability assessment processing of interface simulation to obtain interface simulation stability assessment data; transmit the interface simulation stability assessment data to the terminal to perform interface stability simulation feedback operation of molecular-level surface-modified carbon anode material.

[0070] In this embodiment of the invention, stability assessment of the interface simulation is performed based on dynamic feature data of multidimensional interface properties and ion transport interface degradation feature data. A dual-branch neural network algorithm is used to perform parallel feature analysis on the multidimensional interface property features and degradation features. The two branches extract information on interface structural changes and ion transport degradation, respectively, and key features are concatenated in high dimension through a built-in channel attention mechanism to form high-dimensional feature data related to the stability of the interface simulation. Based on this, coupling analysis is performed on the high-dimensional features to quantify the correlation strength and spatial distribution characteristics between changes in interface microstructure properties and ion transport degradation behavior, forming coupled feature data related to the stability of the interface simulation. According to a preset interface stability assessment index system, the coupled features are evaluated step by step, including interface energy stability, local structural integrity, ion embedding induced stress distribution, and free energy fluctuation amplitude, generating interface simulation stability assessment data. The assessment data is transmitted to a terminal to perform interface simulation feedback operations, which guide the design optimization or further experimental verification of molecular-level surface-modified carbon anode materials.

[0071] Further, the carbon negative electrode material structure data in step S1 includes carbon negative electrode crystal structure type data, carbon negative electrode atomic characteristic data, carbon negative electrode pore distribution data, and carbon negative electrode defect point coordinate data, and the molecular level surface modification design parameter includes modification molecule chemical structure, modification molecule functional group type data, and modification molecule bonding mode data.

[0072] Further, step S1 includes the following steps:

[0073] Step S11: obtaining carbon negative electrode material structure data and molecular level surface modification design parameter;

[0074] In the embodiment of the present application, the three-dimensional atomic arrangement structure of the carbon negative electrode material is obtained through experimental characterization technology and literature data, including the distribution of carbon atoms in the hexagonal lattice, pore structure, surface defect type and boundary structure characteristics. Combined with the molecular level surface modification design parameter, the modification group type, spatial positioning mode, connection chemical anchor point and expected chemical reaction site are determined. The conformation of the carbon negative electrode material surface at the atomic level is analyzed by using high-resolution scanning probe microscopy or atomic force microscope data, and the local geometric configuration information and surface active site distribution of the carbon negative electrode are obtained. The basic input data for subsequent carbon negative electrode atomic coordinate indexing, structure modeling and surface modification docking are provided, and it is ensured that the model can reflect the microstructure characteristics of the real carbon negative electrode surface, including surface curvature, atomic exposure degree and local electron affinity distribution, realizing high-precision atomic level modeling basis.

[0075] Step S12: extracting carbon negative electrode structure coordinate data according to the carbon negative electrode material structure data, indexing and identifying the carbon negative electrode structure coordinate through the carbon negative electrode structure coordinate data, to obtain carbon negative electrode structure coordinate indexing data;

[0076] In the embodiment of the present application, the three-dimensional coordinate value of each carbon atom is generated into a coordinate list by extracting the atomic coordinates from the carbon negative electrode material structure data, and different atoms or atomic clusters are uniquely indexed and identified. The index identification not only records the atomic position, but also annotates the surface exposure degree, local electron density, chemical bond environment and surface defect type. By establishing a coordinate index matrix, the topological relationship and spatial position of atoms in the lattice are mapped, so that the atomic interaction and possible molecular level modification site can be accurately identified when the carbon negative electrode structure model is constructed subsequently. In the operation process, the atomic coordinates are optimized by using molecular dynamics method to minimize the energy, to ensure the physical rationality of the coordinate data, and at the same time to ensure that the index information completely records the spatial accessibility and chemical anchor potential of each atom docking modification group, to provide accurate basis for the spatial docking of molecular level surface modification.

[0077] Step S13: carbon negative electrode structure modeling is performed on the carbon negative electrode material structure data by using the carbon negative electrode structure coordinate index data to obtain a carbon negative electrode structure model;

[0078] In the embodiment of the present application, three-dimensional structure modeling is performed based on carbon negative electrode structure coordinate index data. By establishing covalent bond and interaction force field models between carbon atoms, a complete carbon negative electrode atomic network structure is formed. In the structure modeling process, surface defects, pores and boundary structures are included in the atomic mechanics simulation, and the molecular dynamics simulation method is used to optimize the overall structure of the carbon negative electrode to achieve the state of minimum energy, while maintaining the actual configuration of the surface exposed atoms and the defect area. This step also labels the active sites on the surface of the carbon negative electrode, records the local electron density and the space area of accessible chemical groups, and provides accurate positioning reference for molecular-level surface modification. The carbon negative electrode structure model not only contains the spatial position of atoms, but also contains the chemical modification potential information, which provides a reliable basis for the next step of spatial grid analysis of surface modification.

[0079] Step S14: spatial grid attribute analysis of molecular surface modification is performed based on the carbon negative electrode structure model to obtain spatial grid attribute data of molecular surface modification;

[0080] In the embodiment of the present application, based on the carbon negative electrode structure model, the molecular surface modification space grid attribute analysis is carried out on the surface. First, the surface is covered with refined triangular grid or polyhedral grid, and each grid unit is identified by the grid center position and the adjacent atom set. For each grid unit, local curvature calculation is implemented, the local surface fitting method is used to fit the neighborhood atomic coordinates and extract the principal curvature and average curvature indicators to describe the convex or concave features. Then, the surface exposure is calculated, the spherical contact area measurement method is used to evaluate the unit contact surface area at the grid scale and the exposure degree is represented by the ratio of the number of surface atoms to the contactable area. For the acquisition of local electron affinity approximation, the charge balance model or approximate density functional method is used to estimate the local potential and local charge density distribution on the local atomic cluster, and the difference between the potential minimum point and the local average charge is used to obtain the local electron affinity approximation value. For each grid unit, further energy benchmark scanning is performed, and the potential energy mapping method of superimposing short-range van der Waals potential and electrostatic potential is used to arrange the probe ion in the grid unit to scan the potential energy along the normal and tangential directions, and record the minimum potential energy position and potential energy surface gradient to reflect the rough distribution of adsorption energy and migration potential barrier. Adaptive refinement strategy is implemented for grid resolution: in the high curvature or high exposure area, the grid is further refined and the above calculation is repeated to ensure the accuracy of detail description. The final output of the molecular surface modification space grid attribute data includes grid unit identification, center coordinates, principal curvature and average curvature value, exposure index, local electron affinity approximation value, local potential minimum value and potential gradient, bondable site count and local conformation restriction description, which provides a multi-dimensional quantitative basis for molecular docking and interface interaction evaluation.

[0081] Step S15: performing spatial docking geometry analysis processing of the molecular level surface modification according to the molecular level surface modification design parameters and the molecular surface modification space grid attribute data, to generate molecular level surface modification spatial docking data;

[0082] In the embodiment of the present application, based on the design parameters of molecular-level surface modification and the spatial grid attribute data, spatial docking geometry analysis of molecular-level surface modification is carried out. For each type of modified molecule, its rigid skeleton and rotational freedom are defined, and the sampling interval of the key dihedral angle is set to enumerate the local conformation, generating a candidate conformation set. The candidate conformations are preliminarily coordinated with the grid cells according to the predefined access mode, the interatomic shortest distance between the conformations and the atoms in the target grid cell is calculated, and the geometric conflict is judged: if the distance between the non-bonding atom pairs is less than the collision threshold, it is marked as conflict and removed. The geometric matching degree index is calculated for the non-conflict conformations, and the geometric matching score is constructed by distance error, angle deviation and surface normal consistency. The conformations that pass the geometry are energy scored, and the energy function is composed of short-range van der Waals term, Coulomb electrostatic term and polarization approximation term based on local polarizability. The energy score is used for pre-sorting. The conformations in the front of the sorting are subjected to local energy minimization, allowing the modified molecule to adjust the micro-width of the rotatable bond and relax the access bond length and bond angle. The force field is used to describe the interatomic interaction, and the local conformation is optimized to maintain the integrity of the surface topology. After optimization, the conformations are clustered, grouped according to RMSD or interaction energy similarity, the representative conformation of each cluster is extracted, and the corresponding docking site identifier, pose matrix, energy decomposition term (van der Waals, electrostatic, polarization approximation) and geometric parameters (bond length, bond angle, relative dihedral angle) are recorded. The finally generated molecular-level surface modification spatial docking data provides detailed geometric pose, local stability score and energy decomposition information for subsequent modeling, which is convenient for judging the priority docking state of the modified group on different surface grid cells.

[0083] Step S16: Perform carbon negative electrode modeling processing of molecular-level surface modification based on the molecular-level surface modification spatial docking data and the carbon negative electrode structure model to obtain a molecular-level surface modification carbon negative electrode model.

[0084] In the embodiment of the present application, the final molecular-level surface modification carbon negative electrode model is constructed by using the molecular-level surface modification space docking data and the carbon negative electrode structure model. The representative conformation is embedded in the corresponding grid unit of the carbon negative electrode surface with its pose matrix, and a new bond is added in the atomic topology. The establishment of the new bond is based on the preset bond length threshold and bond angle constraint, and meets the requirements of atomic coordination number and stoichiometry. After the topology is updated, the local geometry and energy optimization are performed on the entire interface region. The force field or reaction type force field that can describe the formation and rupture of chemical bonds is used to physically describe the bond energy item, and energy minimization and short-time finite temperature dynamics relaxation are performed on the system to eliminate conformational stress. The interface charge redistribution is performed during the relaxation process. The charge equalization or charge iteration method is used to redistribute the charge of the interface atoms, and the difference between the initial and final atomic charges is recorded to evaluate the electronic rearrangement effect. After the structure is stabilized, energy decomposition analysis is performed to calculate the interface binding energy and its composition items (van der Waals contribution, electrostatic contribution, polarization contribution), and to output the bond length distribution statistics, bond angle distribution statistics, local stress tensor approximation, and local electron density change description. The finally formed molecular-level surface modification carbon negative electrode model contains complete coordinate topology, bond information, energy and electronic property annotation, and stability score of docking pose, which provides physically consistent and reproducible initial configuration for subsequent ion dynamics multi-scenario simulation and interface stability quantitative evaluation.

[0085] Further, step S14 comprises the following steps:

[0086] Step S141: performing carbon negative electrode structure space specificity analysis according to the carbon negative electrode structure model to obtain carbon negative electrode space specificity data, wherein the carbon negative electrode space surface curvature data, the carbon negative electrode surface exposure data, and the carbon negative electrode local electron affinity approximation data are included.

[0087] In the embodiment of the present application, based on the carbon negative electrode structure model, a surface patch description is constructed, a triangular mesh or a tetrahedral surface is used to approximate the exposed layer of the carbon negative electrode, and the mesh resolution is adaptively refined according to the local atomic density. The curvature calculation is performed on each mesh element, the principal curvature and the average curvature values are obtained by using the neighborhood point quadratic surface fitting method, and the principal curvature direction is obtained by eigenvalue decomposition and recorded in the form of a vector; the curvature calculation simultaneously outputs the curvature gradient field to depict the surface mutation zone. The surface exposure degree is determined by the spherical contact method: a contact ball is arranged along the normal at the center of the mesh, and the exposure degree index is evaluated according to the area ratio of the spherical surface to the surface intersection and the local atomic visible hemisphere coverage, and the atomic accessibility of the surface is evaluated in combination with the neighborhood atomic coordination number. The local electronic affinity is approximately obtained in two steps: one is to perform single-point quantum chemical approximation calculation based on the local atomic cluster to obtain the local electrostatic potential distribution, and the other is to use the charge balance approximation to quickly redistribute the charges of a larger scale atomic cluster to obtain the local charge density. The two parts of results are interpolated and synthesized to form the electronic affinity approximation value of the mesh scale, which is recorded as three indexes of local potential minimum, local charge accumulation and local polarization rate. The above indexes are summarized according to the mesh elements, and are output in the form of vector attributes to provide multi-dimensional criteria for the next mesh division.

[0088] Step S142: performing spatial mesh division processing specific to the molecular surface modification according to the spatial specificity data of the carbon negative electrode, to obtain molecular surface modification specificity spatial mesh data;

[0089] In the embodiment of the present application, the spatial mesh division specific to the molecular surface modification is performed on the surface of the carbon negative electrode according to the obtained curvature, exposure degree and local electronic affinity approximation data. First, a graph structure based on surface adjacency relationship is constructed, the nodes represent the mesh elements, and the edges are connected according to the mesh shared boundary as the connection relationship and weighted according to the geographical distance and curvature similarity. The graph is subjected to partition processing based on spectral decomposition, so that the mesh elements with adjacent and similar attributes are merged into the same mesh cluster; local refinement partition is performed on the regions with high curvature gradient or exposure degree mutation to ensure the boundary accuracy. For each mesh cluster, the representative curvature, representative exposure degree and representative electronic affinity of the cluster center are calculated, and hierarchical labeling is performed based on the representative indexes, including high response layer, medium response layer and low response layer, and the hierarchical determination is completed by using the preset threshold logic. The mesh division result is recorded as the molecular surface modification specificity spatial mesh data, including the member mesh identification of each cluster, the cluster center coordinates, the representative physical and chemical quantities, the atomic count in the cluster, the adjacency relationship network of the cluster and the local shape parameters, which are used for docking geometry analysis.

[0090] Step S143: performing molecular surface modification spatial mesh attribute identification on the molecular surface modification specificity spatial mesh data, to obtain molecular surface modification spatial mesh attribute data.

[0091] In the embodiment of the present application, the specific spatial grid data of the molecular surface modification is subjected to attribute identification to form the spatial grid attribute data of the molecular surface modification. For each grid cluster, the number of potential anchor sites is first evaluated: the number of surface atoms capable of forming covalent bonds is calculated according to the coordination number of atoms in the cluster, the number of lone electrons and the bond breaking index, and the anchor point coordinate list and the coordination constraint set of each anchor point are output. Then, the local non-bonding interaction mapping is performed: a plurality of probe conformations are arranged around the representative point of the cluster along the normal and tangential directions, the potential energy of the probe conformations is evaluated by superimposing the short-range van der Waals potential and the electrostatic potential, and the probe conformations are subjected to local energy minimization to obtain the energy and geometric parameters of the most favorable adsorption pose. The polarization response used for evaluation is obtained by local polarizability estimation, and is coupled with the energy term for correction to reflect the electronic rearrangement effect. In order to describe the spatial hindrance, the solid angle occupancy and the neighborhood blocking index of each anchor site are calculated, and the hindrance metric value and the feasible orientation range are output. Finally, the attributes of each cluster are recorded in a structured form, including the anchor point list and attributes, the local adsorption energy spectrum, the non-bonding energy decomposition term, the steric hindrance, the local electronic affinity curve and the conformational stability description, to form the complete spatial grid attribute data of the molecular surface modification, which provides multi-dimensional physical and chemical constraints for the next precise geometric docking and modeling.

[0092] Further, step S15 comprises the following steps:

[0093] Step S151: performing candidate docking evaluation of the molecular surface modification according to the molecular surface modification spatial grid attribute data and the molecular surface modification design parameters to generate candidate docking evaluation data of the molecular surface modification;

[0094] In the embodiment of the present application, based on the design parameters of the molecular level surface modification and the spatial grid attribute data of the molecular surface modification, a candidate docking set between the modified molecules and each spatial grid cluster is constructed. First, the rigid backbone geometry description, the rotatable bond set, the functional group position and the local charge distribution are extracted from the modified molecules; the curvature, exposure, local electronic affinity approximation, anchor point coordinates and steric hindrance metric are read for each grid cluster. The modified molecules and the grid clusters are subjected to geometric compatibility preliminary screening, and the screening conditions include the angle constraint of the modification main axis and the surface normal, the minimum distance constraint of the functional group to the anchor point, and the minimum non-overlapping distance constraint based on the atomic van der Waals radius. The comprehensive score of each candidate configuration passing the preliminary screening is calculated, and the score is composed of the linear combination of the van der Waals interaction energy, the electrostatic interaction energy, the polarization approximation contribution and the steric conflict penalty term according to the preset weight coefficient; the steric conflict penalty is reflected in the function form of the atomic overlap volume and the overlap volume fraction. Potential energy mapping scanning is performed near the docking site to obtain the minimum value of the adsorption potential energy for energy benchmarking. The candidate docking evaluation results are output in the form of structured entries of candidate pose matrix, energy components, geometric matching degree, anchor mapping table and steric hindrance metric, forming the molecular level surface modification candidate docking evaluation data for subsequent conformation enumeration and local optimization.

[0095] Step S152: performing candidate docking local conformation enumeration simulation processing according to the molecular level surface modification candidate docking evaluation data to obtain the molecular level surface modification finite set conformation data;

[0096] In the embodiment of the present application, based on the molecular level surface modification candidate docking evaluation data, local conformation enumeration is performed for each candidate site to generate a finite set conformation. Discrete sampling intervals are set for each rotatable bond and grid sampling is performed for key dihedral angles, while existing Rotamer sets are applied for common functional groups to cover typical orientations; during the enumeration process, strict collision detection is performed for each new conformation, and the judgment standard is the atomic shortest distance and the overlap volume threshold, and the conformation exceeding the threshold is excluded. The unexcluded conformation is subjected to preliminary energy evaluation by using the molecular force field, and the energy term includes the short-range van der Waals, the Coulomb electrostatic term and the polarization correction based on the local polarizability, and the implicit dielectric approximation is applied to revise the energy of the solvent effect. The conformation with low energy is subjected to local constraint energy minimization, and the constraint is used to maintain the integrity of the key contact bond and contact surface, and the minimization is solved by gradient iteration until the energy gradient converges to the set threshold. In order to enhance the global search ability, the low-energy conformation is subjected to the Monte Carlo annealing local search with temperature disturbance, and the disturbance amplitude and the annealing program are controlled based on the energy stratification. Finally, all the optimized conformations are subjected to clustering processing according to the geometric similarity and the energy, and the representative conformation is extracted from each cluster and the pose matrix, the energy components, the key interaction distance, the contact atom list and the conformation stability index are recorded, forming the molecular level surface modification finite set conformation data.

[0097] Step S153: Optimize the spatial docking geometry conflict of molecular-level surface modification using finite set conformation data of molecular-level surface modification to generate spatial docking data of molecular-level surface modification.

[0098] In this embodiment of the invention, molecular-level surface-modified finite set conformation data is used as input to perform geometric conflict optimization for spatial docking and output the final spatial docking data. For each conformation, a fine collision analysis is first performed to calculate the overlap volume, shortest distance, and local stress index of contacting atomic pairs to quantify the degree of conflict. For conformations with conflicts, progressive geometric adjustments are performed: rotatable bonds are twisted at small angles, and the docking pose is translated and rotated slightly along the surface normal to reduce the overlap volume. Then, after each fine adjustment, the binding energy is minimized to restore local stability. If the overlap still exceeds the conflict threshold after repeated adjustments, a representative conformation from the nearest cluster is used as an alternative pose to replace the original conformation. After conflict resolution, a complete energy decomposition is performed on each final conformation to calculate van der Waals, electrostatic, polarization, and solvent contributions, and further calculate the interface binding energy and the area-normalized binding energy index. For multi-site modification scenarios, coverage optimization is performed to evaluate the mutual repair obstacles and energy coupling between concurrent sites. Site combination optimization is performed through minimum mutual repair spacing constraints and coverage density penalties to obtain feasible multi-site docking schemes. Finally, all conflict-free conformations are sorted by energy and geometric stability and representative poses are extracted by RMSD clustering. The pose matrix, energy components, contact matrix, anchor bond length statistics and local electron rearrangement description of the representative poses are recorded to form complete molecular-level surface modification space docking data, which serves as a reliable initial configuration set for subsequent atomic-level modeling and ion dynamics simulation.

[0099] Furthermore, as an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S2 is shown below. In this embodiment, step S2 includes the following steps:

[0100] Step S21: Perform interface group analysis between the molecular surface and the carbon anode on the molecular-level surface-modified carbon anode model to generate interface group data;

[0101] In the embodiment of the present application, based on the model of carbon negative electrode with molecular-level surface modification, the interface group identification and attribute labeling of the carbon negative electrode and the modified molecular contact interface are carried out. First, the interface region is defined by the interatomic contact criterion: the contact pairs are determined according to the multiple of the nearest neighbor distance and the reference bond length range, the interface atom adjacency table is constructed, and the atomic cluster boundary is identified. For each candidate interface atomic cluster, group template matching and topological identification are implemented, the chemical bond topological rules are used to identify the structural units of hydroxyl, carboxyl, ester, amide, amine, ether, ketone, cyclic aromatic group and oxygen / nitrogen-containing heteroatom fragments, and the defect sites, dangling bonds and vacancy sites existing on the surface of the carbon negative electrode are specially labeled. In order to quantitatively describe the microenvironment of the group, the bond order evaluation of the contact bond is carried out by using the bond order based quantification method, and the bond order is obtained by quantum chemistry or bond order estimation based on the reactive force field, which is used to distinguish the bond strength and partial covalent characteristics. Further, the geometric center, the angle distribution with the surface normal, the minimum distance statistics of adjacent atoms, and the aromaticity index or hydrogen bond donor / acceptor energy level index of each interface group are calculated. In order to reflect the electronic properties, the local charge, local potential and orbital energy level approximation values of the interface cluster are obtained by using the local electronic structure approximation calculation, and these electronic quantities are part of the group attributes. The finally output interface group data includes: group type identification, constituent atom index, geometric positioning and orientation parameters, bond order and bond energy approximation, local charge and potential value, hydrogen bond donor / acceptor index, and candidate contact site list with the surface thin layer, which provides an atomic-level input basis for subsequent interface force field type analysis and bond parameter extraction.

[0102] Step S22: interface group force field type analysis processing is carried out according to the interface group data, and interface group force field type data is generated;

[0103] In the embodiments of the present application, the interface group data is used to determine the type of force field and the parameter family. First, each interface group is mapped to the atomic type family in the force field description module according to the atomic type, coordination environment and bond order characteristics. The mapping is completed according to the chemical environment rules and the force field physical classification criteria. The force field types after mapping include but are not limited to: harmonic oscillator type potential, quadratic harmonic angle potential, Fourier series torsion potential, Lennard-Jones or Buckingham type non-bond potential and atomic type pair of Coulomb electrostatic term. For interfaces containing reactive bonds (such as C-O, C-N bond breaking sites or sulfur content sites), a reactive force field parameter family is used for illustrative annotation to describe the energy curve of the bond formation and breaking process. Then, single-point energy comparison and profile scanning calibration are implemented: using the energy curve obtained by quantum chemistry single-point energy and local geometry scanning as a reference, the bond, angle and torsion parameters of the force field family are adjusted by curve fitting method, so that the local energy surface described by the force field is consistent with the quantum reference to achieve the preset matching degree. The parameter application range and mismatch error are recorded in this process for subsequent uncertainty evaluation. The interface group force field type data generated finally is the force field type identification listed by group, the corresponding atomic type mapping table, the initial value of bond angle and torsion, the non-bond potential parameter family and the parameter calibration performance index, which is used as the parameter input set for subsequent interface mechanics and thermodynamics simulation.

[0104] Step S23: According to the interface group data, the interface bond length, bond angle and dihedral angle parameter analysis processing is performed to generate interface bonding interaction data;

[0105] In the embodiment of the present application, for the interface group data, quantitative analysis of the bond length, bond angle and dihedral angle parameters is carried out to construct the interface bonding interaction data. First, the geometric parameters of each contact group in the static optimal configuration are calculated: the bond length is represented by the shortest distance between atom pairs, and the bond angle and dihedral angle are calculated by the atomic coordinates of the corresponding three atoms and four atoms; for groups with multiple conformations, the equilibrium configuration parameters are obtained after local geometry optimization. Then, the parameter distribution sampling under thermodynamics and dynamics is carried out, the time series of bond length and bond angle are obtained by short-time evolution of finite temperature molecular dynamics, and the mean, variance and higher moments are calculated from the sequence to reflect the bonding elasticity and flexibility under thermal fluctuations. To obtain the bond force constant, the second derivative approximation or the normal mode analysis is implemented, the frequency analysis of the local vibration mode is carried out, and the harmonic oscillator force constant is calculated from the frequency and effective mass; the Fourier expansion is used to fit the torsional energy curve to obtain the torsional potential amplitude and phase term. Energy decomposition is carried out in parallel, the total bonding interaction energy is decomposed into bond energy contribution, local covalent / polarization contribution and local non-bonding energy coupling term, to quantify the contribution of each term to the interface stability. The finally output interface bonding interaction data includes the equilibrium bond length, bond force constant, bond energy component, bond length distribution statistics, related bond angle and dihedral angle parameters and conformation-dependent energy profile of each bond, which provides accurate geometric and energy parameters for subsequent interface mechanical response and energy barrier calculation.

[0106] Step S24: Extracting the molecular-level surface charge parameter and the carbon negative electrode charge parameter by the molecular-level surface modification of the carbon negative electrode model, performing interface charge transport characteristic analysis on the interface group data according to the molecular-level surface charge parameter and the carbon negative electrode charge parameter, and generating interface charge transport characteristic data;

[0107] In the embodiment of the present application, based on the model of carbon negative electrode with molecular-level surface modification, the interface charge distribution and transmission characteristics are comprehensively analyzed. Firstly, two-scale charge solving strategy is implemented for the interface atomic cluster: high-precision quantum chemistry or semi-empirical quantum method is used for local charge calculation of key contact sites and their first-order neighborhood to obtain accurate charge distribution profile, while charge balance method is used for global charge initial allocation of larger scale interface area to ensure charge conservation and consistency of long-range electric field. The two types of charge results are combined into a consistent charge field from local to global through spatial interpolation and overlap weight. Based on the charge field, the interface charge transmission characteristics are calculated: the atomic charge time sequence in the ion intercalation or electron rearrangement process is analyzed in time sequence, the net charge increment and charge flow vector of each group are accumulated, and the interface charge mobility approximation and local potential gradient are further calculated. In order to reflect the charge coupling effect, the change of dipole moment between groups, the potential profile from the interface to the bulk phase and the distribution of electric field strength across the interface are calculated, and the charge attribution according to the group is obtained based on the box volume integral or Bader segmentation method. The finally generated interface charge transmission characteristic data includes the initial and steady-state charge values, net charge transfer amount, instantaneous charge flow density vector, local potential gradient profile and dipole moment evolution curve of each interface group, which provides charge dynamics information for interface non-bond potential regulation and ion transmission energy analysis.

[0108] Step S25: performing interface non-bond potential analysis on the interface bonding interaction data and the interface charge transmission characteristic data according to the preset non-bond potential cutoff distance parameter to obtain interface non-bond potential data, and performing non-bond potential scaling adjustment processing on the interface non-bond potential data by using the preset polarization effect scaling factor to obtain interface scaled non-bond potential data;

[0109] In the embodiment of the present application, based on the interfacial bonding interaction data and the interfacial charge transport characteristic data, the interfacial non-bond potential analysis is carried out under the condition of a preset non-bond potential cutoff distance, and a polarization effect scaling process is applied to obtain interfacial scaled non-bond potential data. First, an interfacial atomic pair adjacency list is constructed, and atomic pairs with a distance less than the cutoff distance are included in the non-bond calculation category. The short-range van der Waals interaction term and the Coulomb electrostatic term are calculated for these atomic pairs. The van der Waals term is expressed by Lennard-Jones or Buckingham type and based on the parameter family attributed in the parameter library, and the electrostatic term is expressed by point charge Coulomb interaction and includes a dielectric shielding model as a correction. Spatial potential mapping is performed on the interface region: the superimposed non-bond potential field is calculated on the two-dimensional grid in the interface normal and tangential directions to obtain the local potential terrain and record the lowest energy channel. Then, a polarization effect scaling factor is introduced to reflect the enhancement or weakening effect of local polarization response on non-bond potential. The factor is constructed based on the function form of local polarizability estimation and charge rearrangement amplitude, and the non-bond potential is multiplied by the scaling factor or the charge is corrected according to the atomic pair or the grid cluster to obtain the scaled non-bond potential distribution. The consistency of the scaled results is checked, the error is evaluated by comparing the local energy extreme value with the quantum reference point, and the correction coefficient is recorded. The finally output interfacial scaled non-bond potential data includes atomic pair non-bond potential matrix, local potential terrain map, minimum potential energy channel, polarization scaling factor value and error evaluation report, which provides a polarization corrected non-bond interaction representation for subsequent barrier and free energy calculation.

[0110] Step S26: Perform interfacial multi-dimensional attribute characteristic analysis of the molecular level surface modified carbon negative electrode according to the interfacial group force field type data, the interfacial bonding interaction data, the interfacial charge transport characteristic data and the interfacial optimized non-bond potential data, and generate interfacial multi-dimensional attribute characteristic data.

[0111] In the embodiment of the present application, the interface group force field type data, interface bonding interaction data, interface charge transport characteristic data and interface optimization non-bond potential data are comprehensively analyzed to generate interface multi-dimensional attribute characteristic data. First, all atomic and group level indicators are normalized to make different physical quantities have comparable scales; the normalization method sets standardization rules according to the physical quantity dimension and statistical distribution and outputs normalized parameters. Then, a multi-dimensional feature vector is constructed, and the feature vector of each interface group or grid cell includes: geometric description (curvature, exposure), chemical description (group type, bond level statistics), mechanical description (bond force constant, local stress estimation), energy description (local binding energy, non-bond potential minimum), charge and polarization description (net charge transfer, dipole moment change, local polarizability), and dynamic related quantities (local migration energy barrier approximation, thermal fluctuation amplitude). Dimensionality reduction and structure analysis are performed on these feature vectors, principal component analysis is used to identify the low-dimensional feature subspace of the dominant interface behavior, and hierarchical clustering is used to group the interface sites according to their similarity to form functional cluster division. In the process of constructing multi-dimensional attributes, energy correlation analysis is also carried out, and the interface binding energy and migration potential barrier, spin / polarization term and bond energy are correlated to obtain the feature weight. The finally generated interface multi-dimensional attribute characteristic data is output in a structured form, which includes the feature vector of each interface group or grid cell, the principal component score, the cluster attribution, the feature weight and the energy coupling coefficient, providing a comprehensive multi-dimensional input basis for subsequent ion transport barrier analysis, interface degradation feature extraction and stability evaluation model.

[0112] Further, step S3 comprises the following steps:

[0113] Step S31: performing carbon negative electrode multi-scenario dynamic response simulation processing on the molecule-level surface modified carbon negative electrode model according to the preset battery system embedding parameters, to generate carbon negative electrode multi-scenario dynamic response simulation data;

[0114] In the embodiments of the present application, according to the preset battery system embedding parameters, the multi-scene dynamic response simulation of the molecular-level surface modified carbon negative electrode model is carried out. The preset battery system embedding parameters include but are not limited to: electrode potential boundary condition, interface charge constraint strategy, electrolyte composition and ion chemical potential, temperature field distribution, external electric field or current density time sequence, solvation layer thickness and interface contact pressure, etc. Based on the above parameters, an independent simulation scene is established for each physical working condition, and the scene type covers different potential driving, different electrolyte ion species, temperature gradient and load curve of the cycle charging and discharging stage. In each scene, a numerical solution process based on molecular dynamics is adopted, the electrode is described by a constant potential method, the electrode surface atoms are allowed to have variable charges to maintain the set potential according to the charge balance algorithm, and the grid Ewald scheme is used for accurate summation of long-range electrostatic interaction. The interatomic force is described by a parameterized reactive force field or a high-fidelity force field to cover bond formation / breaking and polarization response. The simulation process sets different ensembles (such as NVT or NPT) according to the thermodynamic statistics cluster, and applies the Nosé-Hoover or Langevin heat bath algorithm to maintain the temperature field, and enables the external field driving term when needed to simulate the electrochemical excitation. The solvent and ions at the interface are explicitly represented by molecules, and the time-resolved atomic coordinates, velocities, forces, local charges and system energy items are recorded, and the output is a carbon negative electrode multi-scene dynamic response simulation data package, which contains the time sequence trajectory frame, energy item, interface contact statistics, ion coordination state time evolution and temperature / pressure time curve of each scene, as the original input for subsequent ion dynamics analysis.

[0115] Step S32: performing multi-scene ion dynamic response trajectory analysis according to the carbon negative electrode multi-scene dynamic response simulation data to generate multi-scene ion dynamic response trajectory data;

[0116] In the embodiment of the application, based on the carbon negative electrode multi-scene dynamic response simulation data, systematic trajectory analysis is performed on the ion dynamic behavior in each scene. First, the trajectory is preprocessed, including coordinate translation, alignment with interface reference system and time sequence synchronization, then the single ion path is extracted and key events such as adsorption, desorption, penetration, embedding and dissociation are identified by spatial threshold and coordination number mutation criterion. Statistical quantities are calculated for each path: residence time distribution, average migration distance, mean square displacement (MSD) curve and local diffusion coefficient, coordination number and coordination exchange frequency are represented by time autocorrelation function. Through sampling enhancement technology, potential barrier information is obtained, and potential energy curve along the typical reaction coordinate is obtained by using constrained sampling or parallel traction path, and weighted histogram analysis or multiple state reweighting method is used to reconstruct potential mean function (PMF), and the local energy barrier and steady-state binding energy are obtained by PMF. Path clustering is based on predetermined collective variables (such as ion to surface distance, coordination number and local charge) to perform unsupervised clustering of trajectories, thereby identifying typical transmission pathways and intermediate states. In order to obtain the kinetic rate, a Markov state model is constructed, and the inter-state rate is estimated by using the transition count matrix of the trajectory and the lifetime and transition path entropy are calculated. The output multi-scene ion dynamic response trajectory data finally contains: time sequence event log grouped by scene and ion, geometric and energy profile of each path, PMF curve and local energy barrier, rate matrix obtained by MSM and path clustering results, for subsequent cross-scene integrated analysis.

[0117] Step S33: The multi-scene ion dynamic response trajectory data is processed by trajectory integration to obtain integrated ion dynamic response trajectory data.

[0118] In this embodiment of the invention, trajectory integration processing is performed on multi-scenario ion dynamic response trajectory data to construct a unified integrated ion dynamic response trajectory dataset. First, spatial and temporal standardization is performed: several stable anchor points on the carbon anode surface are selected as reference benchmarks, and all trajectories undergo coordinate transformation and rotation alignment according to this surface reference system. Then, the trajectories are resampled at a unified time step, and the original sampling density is recorded. Statistical deviations between different scenarios are addressed using a reweighting method. The trajectory contribution is weighted according to the physical weighting factor of the scenario (determined by the probability of battery operating conditions or engineering attention). A multi-reweighting self-consistent method (e.g., MBAR-like methods) is used to unbiasedly merge the free energy estimates from different sampling windows. During the integration process, consistency checks are performed on the trajectory metadata: energy conservation checks, local charge conservation checks, and contact count consistency checks are performed, eliminating abnormal segments with energy mutations exceeding thresholds or trajectory breaks. After alignment and weighting, cross-scenario collective variable reconstruction is performed. A set of common collective variables is extracted based on principal component analysis or adversarial autoencoding methods to compare migration channels under different scenarios in a unified low-dimensional space. The final generated integrated ion dynamic response trajectory data is output in the form of a standardized trajectory library, which includes a standardized set of trajectory frames, event annotation index, cross-scene clustering labels, re-weighted reconstruction of global PMF and energy barrier statistics, and contribution decomposition of scene weights to the frequency of various events. This provides a fused high-confidence trajectory foundation for interface dynamic feature analysis, energy field construction, and interface degradation factor quantification.

[0119] Furthermore, as an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 1 A detailed flowchart of step S4 is shown below. In this embodiment, step S4 includes the following steps:

[0120] Step S41: Based on the integrated ion dynamic response trajectory data, perform dynamic change analysis on the interface multidimensional attribute feature data to generate interface multidimensional attribute dynamic feature data.

[0121] In the embodiment of the present application, based on the integrated ion dynamic response trajectory data, time sequence coupling analysis is performed on the previously generated interface multi-dimensional attribute feature data to obtain the dynamic change characteristics of the interface multi-dimensional attributes. First, the integrated trajectory is corresponded to the interface multi-dimensional attribute vector at the spatial grid scale, so that the ion occupation information, local charge distribution and atomic conformation change corresponding to each time frame are mapped to the corresponding grid cells; then, a time series vector set is constructed for each grid cell, including curvature, exposure, bond length fluctuation, local charge and non-bond potential value, etc. Sliding window statistics are performed on these time series to extract instantaneous mean, variance, skewness and autocorrelation function, and slow time scale dominant mode is identified through time lag independent component analysis (tICA) or principal component analysis (PCA); further, cross-correlation matrix between grids and transfer entropy between grid points are calculated to quantify the causal coupling strength between attribute change and ion activity. Wavelet transform is performed on the high-frequency fluctuations to obtain time-frequency spectrum characteristics, and the characteristic time constant of the slow mode is extracted. The output interface multi-dimensional attribute dynamic feature data includes time series statistics, principal component score trajectory, coupling matrix between grids and frequency spectrum characteristics of each grid cell, which are used to describe the dynamic response behavior of the interface under the action of ion load and thermal excitation.

[0122] Step S42: ion embedding deposition distribution analysis on the interface is performed according to the integrated ion dynamic response trajectory data to obtain interface ion embedding deposition distribution data;

[0123] In the embodiment of the present application, ion embedding deposition distribution analysis on the interface is carried out according to the integrated ion dynamic response trajectory data. First, the normal direction and local coordinate system are defined on the interface, the ion trajectory is projected onto the interface grid and the depth layer interval is established along the normal direction, the time-averaged occupation density field is constructed on the three-dimensional grid by kernel density estimation method, and the joint occupation distribution of the interface two-dimensional in-plane and normal depth direction is obtained. The occupation density field is statistically partitioned, the average occupation number, residence time distribution, first arrival time distribution and escape rate of each grid cell at different depth layers are calculated; at the same time, the radial distribution function (RDF) and coordination number time series of ions near the interface are calculated to represent the local coordination environment. In order to identify the deposition hot spot, threshold segmentation and density peak detection are used to extract high occupation clusters, and event annotation (adsorption, deposition, re-overflow) is performed on the ion residence trajectory in the cluster. The output interface ion embedding deposition distribution data includes three-dimensional occupation density field, occupation profile along depth, residence time matrix, high occupation cluster list and statistical characteristics of each cluster, which provides a spatial distribution basis for subsequent energy field construction and gradient analysis.

[0124] Step S43: deposition distribution gradient feature analysis is performed according to the interface ion embedding deposition distribution data to obtain interface ion embedding deposition distribution gradient feature data, and the interface ion embedding deposition energy field data is designed through the interface ion embedding deposition distribution gradient feature data;

[0125] In the embodiment of the present application, the deposition distribution gradient characteristic analysis is performed based on the deposition distribution data of the interface ion intercalation, and the interface ion intercalation deposition energy field is designed accordingly. Firstly, the gradient vector field and the gradient modulus are calculated by applying the finite difference or spectral method to the occupation density field on the spatial grid, and the density gradient distribution graph along the normal and tangential directions is obtained, and the deposition flow direction and the aggregation boundary are identified from the gradient information. Based on the statistical mechanics relationship, the Boltzmann inversion method is used to map the steady-state occupation density to the approximate potential energy surface, and the noise caused by the occupation fluctuation is regularized to ensure the continuity and differentiability of the potential energy field. In order to improve the physical consistency of the potential energy field, the local polarization and electrostatic contribution are coupled and corrected, and the potential energy is adjusted by the local charge density and the polarization rate field. The final output of the interface ion intercalation deposition energy field data is a smooth and continuous three-dimensional potential energy field, which includes the potential energy contour surface, the geometric trajectory of the minimum potential energy channel, the local potential extreme point position and the gradient field distribution, as the energy basis for the potential barrier analysis and the transmission path search.

[0126] Step S44: performing the potential barrier characteristic analysis of the ion transmission at the interface based on the interface ion intercalation deposition energy field data and the integrated ion dynamic response trajectory data, to generate the ion transmission interface potential barrier characteristic data;

[0127] In the embodiment of the present application, the potential barrier characteristic analysis of the integrated ion dynamic response trajectory is performed based on the interface ion intercalation deposition energy field to generate the ion transmission interface potential barrier characteristic data. Firstly, the minimum energy path search is performed on the energy field, the grid shortest path or energy hill climbing method is used to identify the minimum energy channel from the initial adsorption state to the intercalation state or the penetration state, and the local maximum potential value on the path is extracted as the energy barrier height; the hill climbing-saddle point positioning method is used to determine the saddle point position and calculate the potential value. Combined with the actual path passed in the trajectory, the number of times, the passing time and the energy span distribution through each path are counted, and then the potential barrier height distribution and the corresponding empirical distribution function are obtained. The temperature and external field influence are considered for different scenes, and the scene weighted potential barrier statistics are calculated. The output ion transmission interface potential barrier characteristic data includes the potential barrier height, the saddle point coordinates, the path length, the path passing frequency and the energy barrier distribution statistics of each identified path, which provides key energy parameters for the kinetic rate estimation and the degradation risk assessment.

[0128] Step S45: performing the free energy characteristic analysis of the ion transmission at the interface based on the interface multi-dimensional attribute dynamic characteristic data and the ion transmission interface potential barrier characteristic data, to obtain the ion transmission interface free energy characteristic data;

[0129] In the embodiment of the present application, based on the interface multi-dimensional attribute dynamic characteristic data and the ion transmission interface potential barrier characteristic data, free energy characteristic analysis is carried out to obtain the interface free energy characteristic data of ion transmission. A collective variable mapping strategy is adopted, and a collective variable group (such as ion-to-surface distance, coordination number and local charge) directly related to transmission is selected. The free energy surface is reconstructed on these collective variable spaces. The method includes constructing a Markov state model or reconstructing a potential mean force function (PMF) by trajectory reweighting, and then calculating the free energy baseline, the inter-state free energy difference and the free energy valley depth. In order to separate the entropy and enthalpy contributions, temperature coefficient analysis is performed on the PMF obtained at different temperatures to obtain free energy decomposition; and the local vibration frequency is estimated by quasi-Hamilton or quasi-harmonic approximation to supplement the entropy term calculation. The output ion transmission interface free energy characteristic data includes free energy surface grid, free energy difference of key state, entropy / enthalpy decomposition term, free energy valley width and free energy baseline value of inter-state transition rate, which provides a complete free energy image for interface thermodynamic stability evaluation.

[0130] Step S46: performing interface degradation characteristic analysis of ion transmission according to the ion transmission interface potential barrier characteristic data and the ion transmission interface free energy characteristic data, and generating ion transmission interface degradation characteristic data.

[0131] In the embodiment of the present application, the interface degradation characteristic analysis is carried out by comprehensively considering the ion transmission interface potential barrier characteristic and the free energy characteristic, and the ion transmission interface degradation characteristic data is generated. First, according to the transition state theory or the improved Arrhenius expression, the potential barrier height and the free energy difference are converted into the estimated value of the local event occurrence rate. The event rate is expressed in the form of a frequency factor and an exponential term. The frequency factor is obtained by local normal mode analysis or estimated from the trajectory short-time vibration spectrum. The degradation rate distribution on the time scale of different grid units and paths is calculated and the interface degradation rate map is constructed. Then, energy spectrum clustering is performed: the local potential barrier and the free energy spectrum are grouped by using a clustering algorithm to obtain several typical energy spectrum clusters, and the degradation mechanism label of each cluster (such as bond breaking caused by embedding, local stress accumulation or polarization-induced electron injection) is associated. Finally, the output ion transmission interface degradation characteristic data includes local degradation rate map, energy spectrum clustering result and representative spectrum, dominant degradation mechanism description of each region, activation energy and predicted lifetime estimation of key sites, and degradation risk score for priority sorting, which provides quantitative decision basis for subsequent interface modification strategy and experimental verification.

[0132] Further, step S46 includes the following steps:

[0133] According to the ion transmission interface barrier characteristic data and the ion transmission interface free energy characteristic data, interface degradation influencing factor analysis of ion transmission is performed to obtain ion transmission interface degradation influencing factor data, and the ion transmission interface degradation influencing factor data is used as an interface degradation clustering correlation parameter to perform interface degradation energy spectrum clustering processing on the ion transmission interface barrier characteristic data and the ion transmission interface free energy characteristic data, and ion transmission interface degradation energy spectrum clustering data is obtained.

[0134] According to the ion transmission interface degradation energy spectrum clustering data, interface degradation characteristic analysis of ion transmission is performed to generate ion transmission interface degradation characteristic data.

[0135] In the embodiment of the present application, the influence factors of interface degradation are quantitatively analyzed by taking the ion transmission interface potential barrier characteristic data and the ion transmission interface free energy characteristic data as inputs. The parameters such as the barrier height, the saddle point position, the path curvature, the free energy valley depth, the free energy difference, and the entropy / enthalpy decomposition are projected into a multi-dimensional feature space to construct a feature vector matrix. Subsequently, the core factors affecting the stability of the ion transmission interface are identified through variance contribution rate analysis and sensitivity analysis. For example, if the free energy difference fluctuates more than a certain threshold (such as 10%) at different temperatures, it is marked as a high sensitivity factor; if the correlation coefficient of the local barrier height and the trajectory passing frequency exceeds a preset threshold (such as 0.8), it is determined that it has a significant contribution to the degradation event. Further, the quantitative mapping between the factors and the degradation risk is established by using principal component regression or mutual information quantification method, and the interface degradation influence factor data is obtained. The data includes the weight coefficients of each energy parameter, the contribution degree sorting, the interaction matrix between factors, and the degradation risk index, which provides quantitative input conditions for subsequent clustering processing. After obtaining the interface degradation influence factor data, the ion transmission interface potential barrier characteristic data and the free energy characteristic data are subjected to energy spectrum clustering processing by taking the data as the correlation parameters of clustering. All energy-related features are mapped to a unified energy spectrum space, and the energy spectrum vector includes parameters such as barrier height, saddle point energy, free energy difference, valley width, entropy term, and enthalpy term. These parameters are weighted and standardized by the weight in the influence factor data to ensure that high-contribution factors dominate in clustering. Subsequently, the sample energy spectrum is automatically classified by using a density-based clustering algorithm (DBSCAN) or an energy spectrum Gaussian mixture model, forming several degradation energy spectrum clusters. The energy spectrum morphology in each cluster is extracted to generate a typical energy spectrum curve and its corresponding stability label. For example, if the cluster features a high-potential barrier-deep free energy valley combination, it is determined to be “stable-difficult migration type”; if it features a low-potential barrier-shallow valley combination, it is labeled as “high migration-easy degradation type”. The final interface degradation energy spectrum clustering data includes the number of clusters, the representative energy spectrum in the cluster, the distance matrix between clusters, and the corresponding risk level division. Based on the clustering results, further degradation characteristic analysis is carried out, and the ion transmission interface degradation characteristic data is generated, which provides a quantitative reference framework for evaluating the stability of the material interface and the modification direction.

[0136] Further, step S5 includes the following steps:

[0137] Step S51: Perform double-branch stability correlation feature analysis on the interface simulation of the interface multi-dimensional attribute dynamic characteristic data and the ion transmission interface degradation characteristic data by using the preset double-branch neural network algorithm, generate double-branch stability correlation characteristic data of the interface simulation; perform stability correlation feature high-dimensional feature splicing processing on the double-branch stability correlation characteristic data of the interface simulation by using the channel attention mechanism built in the double-branch neural network algorithm, generate interface simulation stability correlation feature splicing data; perform stability correlation coupling feature analysis on the interface simulation stability correlation feature splicing data, generate interface simulation stability correlation coupling feature data;

[0138] In the embodiment of the application, a double-branch neural network structure is constructed with the interface multi-dimensional attribute dynamic characteristic data and the ion transmission interface degradation characteristic data as inputs. The first branch is used for time sequence feature extraction, and a multi-layer time convolution or a self-attention encoder stack is used to extract features of the time sequence of the interface multi-dimensional attribute, and a time sequence embedding vector representing slow mode and fast mode response is output. The second branch is used for spatial-energy feature representation, and a graph convolution or point cloud convolution module is used to locally encode the topological structure of the interface grid or group graph, and a graph embedding vector representing local degradation state and energy spectrum structure is output. The two branches are sequentially input into a channel attention unit for channel-level reweighting processing. The channel attention adopts a global convergence-bottleneck full connection-activation-relabeling process to obtain the importance weight coefficient of each channel information and output weighted features. The high-dimensional splicing processing is performed on the weighted features, the channel-by-channel splicing is performed, and then the high-order coupling relationship between the two branches is captured through bilinear interaction or low-rank bilinear pooling to obtain a high-dimensional feature matrix of stability correlation. Batch normalization and principal component reduction are performed on the high-dimensional matrix to control the dimension, and the interface simulation double-branch stability correlation characteristic data is mapped to the output end through a full connection layer, including the coupling strength vector of each grid element / group, the channel importance vector and several discriminative embedding components. In the training stage, a supervised regression and sorting mixed loss is used, the regression term is used to fit the mapped stability index, the sorting term is used to promote the differentiation of high-risk sites, and the loss function is combined with L2 regularization and early stopping strategy to avoid overfitting. The training samples are established by historical high-fidelity simulation results or experimentally labeled interface failure cases, and cross-validation and leave-out set testing are performed in the training process to evaluate the generalization performance.

[0139] Step S52: Perform interface simulation stability evaluation processing on the interface simulation stability correlation coupling feature data by using the preset interface stability evaluation index, to obtain interface simulation stability evaluation data;

[0140] In the embodiment of the present application, based on the preset interface stability evaluation index system, the stability correlation coupling feature data of the interface simulation output in step S51 is subjected to evaluation mapping processing. The evaluation index system contains numerical items: interface binding energy density, local free energy difference, local stress amplitude, ion embedding rate, mean residence time and polarization response amplitude, and the physical units and normalization specifications of each index are given. First, the coupling feature vector is standardized to unify the scale to meet the comparability of physical quantities; then the coupling features are mapped to the predicted values of each evaluation index through a regression model or a multi-objective regressor, the regressor uses an integrated regression framework and uses cross-validation to determine the combination weight of the regressor to ensure robustness. The predicted values of each index are subjected to multi-attribute comprehensive scoring, and the weight sum method or the analytic hierarchy process is used to obtain a single-point comprehensive stability score, which reflects the overall stability tendency of the interface under the predicted working condition. At the same time, the prediction results are subjected to uncertainty quantification, and the bootstrap resampling method is used to estimate the confidence interval or the Bayesian regression is used to obtain the posterior distribution, so as to obtain the confidence range of each stability index. The output interface simulation stability evaluation data includes: point estimate and confidence interval of each evaluation index, comprehensive stability score, index contribution degree decomposition (sensitivity of each coupling feature to the index) and several interpretability reports (such as feature importance ranking and local influence diagram), which are used to support engineering decision-making and subsequent modification feedback links. The evaluation results are recorded through prediction error statistics (R square, root mean square error, classification accuracy, etc.) in quality control and used for subsequent model recalibration.

[0141] Step S53: transmitting the interface simulation stability evaluation data to the terminal to perform the interface stability simulation feedback operation of the molecular-level surface modification of the carbon negative electrode material.

[0142] In the embodiment of the present application, the interface simulation stability evaluation data is transmitted to the terminal execution unit in the form of a structured instruction sheet to carry out the simulation feedback operation of molecular-level surface modification. The instruction sheet includes sample identification, interface model index, each stability index value and confidence interval, comprehensive stability score, key risk site list, and factor contribution and priority recommendation of each site (based on index contribution ranking) in a unified mode. To ensure information interoperability and traceability, the instruction sheet adopts a self-describing field format with a timestamp and version number to ensure that the execution unit updates the simulation parameters according to the specified reference state or arranges further multi-scenario repeated simulation. The transmission process adopts an encrypted link to ensure integrity and security, and the receiving end performs format checking and consistency checking on the instruction sheet, including field consistency, physical quantity unit consistency and numerical value boundary checking. The receiving end arranges the feedback operation according to the priority specified in the instruction sheet after passing the check: including local refinement simulation for high-risk sites, parallel comparison simulation of different modification strategies, and life prediction tasks under long-term fatigue working conditions. After each feedback operation is executed, a feedback result sheet is generated and consistency comparison is performed with the initial evaluation sheet to form a closed-loop verification record, which includes the stability change after modification, error evaluation and a number of traceable parameter change logs, thereby realizing the complete closed-loop management process of simulation evaluation-execution-verification.

[0143] Therefore, embodiments should be considered in all respects as illustrative and not restrictive, the scope of the application being defined by the appended claims rather than the description preceding them, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

[0144] The above description is merely that of a particular embodiments of the application and various modifications can be made therein by those skilled in the art without departing from the spirit or scope of the application. Therefore, the present application is not limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of simulating the interfacial stability of a molecularly-graded surface-modified carbon anode material, characterized by, The method comprises the following steps: Step S1: obtaining carbon negative electrode material structure data and molecular level surface modification design parameters; Based on the carbon negative electrode material structure data and the molecular level surface modification design parameters, the carbon negative electrode modeling processing of the molecular level surface modification is carried out to obtain the molecular level surface modification carbon negative electrode model; Step S2: analyzing the interface group data of the molecular level surface modification carbon negative electrode model, and generating the interface group data based on the interface group data; based on the interface group data, the interface multidimensional attribute feature analysis of the molecular level surface modification carbon negative electrode is carried out, and the interface multidimensional attribute feature data is generated; Step S3: through the preset battery system embedding parameter, the ion dynamic trajectory integrated analysis processing of the carbon negative electrode multi-scene simulation running of the molecular level surface modification carbon negative electrode model is carried out, and the integrated ion dynamic response trajectory data is generated; Step S4: according to the integrated ion dynamic response trajectory data, the dynamic change analysis of the interface multidimensional attribute feature data is carried out, and the interface multidimensional attribute dynamic feature data is generated; based on the interface multidimensional attribute dynamic feature data and the integrated ion dynamic response trajectory data, the interface degradation feature analysis of ion transmission is carried out, and the ion transmission interface degradation feature data is generated; Step S5: based on the interface multidimensional attribute dynamic feature data and the ion transmission interface degradation feature data, the stability evaluation processing of the interface simulation is carried out to obtain the interface simulation stability evaluation data; the interface simulation stability evaluation data is transmitted to the terminal to perform the interface stability simulation feedback work of the molecular level surface modification carbon negative electrode material.

2. The method of claim 1, wherein the method is characterized by, The carbon negative electrode material structure data of step S1 includes carbon negative electrode crystal structure type data, carbon negative electrode atomic characteristic data, carbon negative electrode pore distribution data and carbon negative electrode defect point coordinate data, and the molecular level surface modification design parameters include modification molecule chemical structure, modification molecule functional group type data and modification molecule bonding mode data.

3. The method of claim 2, wherein the method is characterized by, Step S1 comprises the following steps: Step S11: obtaining carbon negative electrode material structure data and molecular level surface modification design parameters; Step S12: extracting carbon negative electrode structure coordinate data according to the carbon negative electrode material structure data, and indexing the carbon negative electrode structure coordinate data to obtain carbon negative electrode structure coordinate index data; Step S13: using the carbon negative electrode structure coordinate index data to model the carbon negative electrode structure of the carbon negative electrode material structure data to obtain the carbon negative electrode structure model; Step S14: based on the carbon negative electrode structure model, the molecular surface modification space grid attribute analysis is carried out to obtain the molecular surface modification space grid attribute data; Step S15: based on the molecular level surface modification design parameters and the molecular surface modification space grid attribute data, the spatial docking geometry analysis processing of the molecular level surface modification is carried out to generate the molecular level surface modification spatial docking data; Step S16: using the molecular level surface modification spatial docking data and the carbon negative electrode structure model to carry out the carbon negative electrode modeling processing of the molecular level surface modification to obtain the molecular level surface modification carbon negative electrode model.

4. The method of claim 3, wherein the method is characterized by, Step S14 comprises the following steps: Step S141: performing carbon negative electrode structure space specificity analysis according to the carbon negative electrode structure model to obtain carbon negative electrode space specificity data, wherein the carbon negative electrode space surface curvature data, the carbon negative electrode surface exposure data, and the carbon negative electrode local electron affinity approximation data; Step S142: performing molecular surface modification specificity space grid division processing according to the carbon negative electrode space specificity data to obtain molecular surface modification specificity space grid data; Step S143: performing molecular surface modification space grid attribute identification on the molecular surface modification specificity space grid data to obtain molecular surface modification space grid attribute data.

5. The method of claim 3, wherein the method is characterized by, Step S15 includes the following steps: Step S151: performing candidate docking evaluation of the molecular level surface modification according to the molecular level surface modification space grid attribute data and the molecular level surface modification design parameters to generate molecular level surface modification candidate docking evaluation data; Step S152: performing candidate docking local conformation enumeration simulation processing according to the molecular level surface modification candidate docking evaluation data to obtain molecular level surface modification finite set conformation data; Step S153: performing spatial docking geometry conflict optimization of the molecular level surface modification through the molecular level surface modification finite set conformation data to generate molecular level surface modification spatial docking data.

6. The method of claim 1, wherein the method is characterized by, Step S2 includes the following steps: Step S21: performing interface group analysis of the molecular level surface modification carbon negative electrode model to generate interface group data; Step S22: performing interface group force field type analysis processing according to the interface group data to generate interface group force field type data; Step S23: performing interface bond length, bond angle, and dihedral angle parameter analysis processing according to the interface group data to generate interface bonding interaction data; Step S24: extracting molecular level surface charge parameters and carbon negative electrode charge parameters through the molecular level surface modification carbon negative electrode model, and performing interface charge transport feature analysis on the interface group data according to the molecular level surface charge parameters and the carbon negative electrode charge parameters to generate interface charge transport feature data; Step S25: performing interface non-bond potential analysis on the interface bonding interaction data and the interface charge transport feature data according to a preset non-bond potential cutoff distance parameter to obtain interface non-bond potential data, and performing non-bond potential scaling adjustment processing on the interface non-bond potential data using a preset polarization effect scaling factor to obtain interface scaled non-bond potential data; Step S26: performing interface multi-dimensional attribute feature analysis of the molecular level surface modification carbon negative electrode according to the interface group force field type data, the interface bonding interaction data, the interface charge transport feature data, and the interface optimized non-bond potential data to generate interface multi-dimensional attribute feature data.

7. The method of claim 1, wherein the method is characterized by, Step S3 includes the following steps: Step S31: performing carbon negative electrode multi-scenario dynamic response simulation processing on the molecular level surface modification carbon negative electrode model according to a preset battery system embedding parameter to generate carbon negative electrode multi-scenario dynamic response simulation data; Step S32: performing multi-scenario ion dynamic response trajectory analysis according to the carbon negative electrode multi-scenario dynamic response simulation data to generate multi-scenario ion dynamic response trajectory data; Step S33: Trajectory integration processing is performed on the multi-scene ion dynamic response trajectory data to obtain integrated ion dynamic response trajectory data.

8. The method of claim 1, wherein the method is characterized by, Step S4 includes the following steps: Step S41: Dynamic change analysis of the interface multi-dimensional attribute feature data is performed according to the integrated ion dynamic response trajectory data, and interface multi-dimensional attribute dynamic feature data is generated; Step S42: Ion embedding and deposition distribution analysis on the interface is performed according to the integrated ion dynamic response trajectory data to obtain interface ion embedding and deposition distribution data; Step S43: Deposition distribution gradient feature analysis is performed according to the interface ion embedding and deposition distribution data to obtain interface ion embedding and deposition distribution gradient feature data, and interface ion embedding and deposition energy field data is designed through the interface ion embedding and deposition distribution gradient feature data; Step S44: Ion transmission interface barrier feature data is generated by performing ion transmission interface barrier feature analysis on the integrated ion dynamic response trajectory data according to the interface ion embedding and deposition energy field data; Step S45: Ion transmission interface free energy feature data is obtained by performing ion transmission interface free energy feature analysis according to the interface multi-dimensional attribute dynamic feature data and the ion transmission interface barrier feature data; Step S46: Ion transmission interface degradation feature data is generated by performing ion transmission interface degradation feature analysis according to the ion transmission interface barrier feature data and the ion transmission interface free energy feature data.

9. The method of claim 8, wherein the method is characterized by, Step S46 includes the following steps: According to the ion transmission interface barrier feature data and the ion transmission interface free energy feature data, ion transmission interface degradation influencing factor analysis is performed to obtain ion transmission interface degradation influencing factor data, and ion transmission interface degradation energy spectrum clustering data is obtained by performing ion transmission interface degradation energy spectrum clustering processing on the ion transmission interface barrier feature data and the ion transmission interface free energy feature data through the ion transmission interface degradation influencing factor data as the interface degradation clustering correlation parameter; According to the ion transmission interface degradation energy spectrum clustering data, ion transmission interface degradation feature analysis is performed to generate ion transmission interface degradation feature data.

10. The method of claim 1, wherein the method is characterized by: Step S5 includes the following steps: Step S51: Interface simulation double-branch stability correlation feature analysis is performed on the interface multi-dimensional attribute dynamic feature data and the ion transmission interface degradation feature data by using a preset double-branch neural network algorithm to generate interface simulation double-branch stability correlation feature data; stability correlation feature high-dimensional feature splicing processing is performed on the interface simulation double-branch stability correlation feature data through the channel attention mechanism built in the double-branch neural network algorithm to generate interface simulation stability correlation feature splicing data; and stability correlation coupling feature analysis of interface simulation is performed according to the interface simulation stability correlation feature splicing data to generate interface simulation stability correlation coupling feature data; Step S52: Interface simulation stability evaluation processing is performed on the interface simulation stability correlation coupling feature data by using a preset interface stability evaluation index to obtain interface simulation stability evaluation data; Step S53: transmit the interface simulation stability evaluation data to the terminal to perform interface stability simulation feedback operation of the molecular level surface modification carbon negative electrode material.

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