Method for predicting coverage degree of H ion negative electrode in Zn aqueous battery

Abstract

The invention discloses a method for predicting the coverage degree of an H ion negative electrode in a Zn aqueous battery, and relates to the technical field of surface catalysis. In order to solve the defect that in the prior art, it is difficult to accurately predict the coverage degree of H ions on the surface of a Zn negative electrode on a multi-scale level, the technical scheme provided by the invention comprises the steps that a three-dimensional atom structure model of H atoms and a Zn electrode adsorption surface is constructed, formation energy and adsorption energy of the H ions and water molecules are calculated, and a reaction potential energy surface is established; constructing a reaction path and an electron transfer behavior, defining potential related parameters, and outputting reaction rate, current density and coverage data; establishing a phase field dilute material transfer model containing temperature, pH and pressure parameters, and completing material migration and charge distribution calculation; interface physical field parameters are fed back to the microdynamics model to form microcosmic-macroscopic bidirectional data interaction and realize self-consistent convergence; and outputting a coupling model result to obtain multi-scale prediction data. The method is suitable for active site identification and performance prediction in electro-catalytic reaction.

Description

Technical Field

[0001] This relates to the field of surface catalysis technology, specifically to a method for predicting the H ion coverage in the negative electrode of a Zn aqueous battery. Background Technology

[0002] Against the backdrop of continuously growing energy demand and increasing emphasis on sustainable energy, efficient, safe, and environmentally friendly energy storage systems have become a key pursuit in scientific research and industry. Aqueous zinc batteries, with their numerous outstanding advantages, are gradually becoming the focus of the energy storage field and are considered a highly promising next-generation large-scale energy storage technology solution. The significant advantages of aqueous zinc batteries set them apart from other battery technologies. From a resource perspective, zinc is abundant and widely distributed in the Earth's crust, significantly reducing costs compared to rare metals like lithium and cobalt, providing a solid material foundation for large-scale applications. Simultaneously, using water as the core electrolyte replaces the flammable and explosive organic electrolytes found in traditional lithium batteries, significantly improving battery safety and fundamentally reducing the probability of serious safety hazards such as short circuits, combustion, and even explosions. Furthermore, aqueous zinc batteries excel in environmental protection; their production and use cause minimal pollution, and the recycling of waste batteries is relatively simple, aligning with the current global advocacy for green development. In terms of theoretical performance, zinc metal has a high theoretical specific capacity, meaning it can store more electrical energy, enabling high-energy-density battery systems. These combined advantages make aqueous zinc batteries a promising candidate for applications in renewable energy storage (such as energy storage for solar and wind power), distributed grid energy storage, and power supplies for some mobile devices with relatively less stringent energy density requirements. However, like many emerging technologies, aqueous zinc batteries face a series of severe challenges on their path to large-scale commercial application. Among these, the instability of the zinc anode during charge and discharge is particularly prominent, severely restricting the battery's long-term cycle performance and practical applications. Of the many factors affecting the stability of the zinc anode, the hydrogen evolution reaction (HER) has become a key obstacle. The occurrence of hydrogen evolution reaction (HER) in aqueous zinc batteries primarily stems from the abundant water molecules present in the aqueous electrolyte. During battery charging and discharging, especially at the negative electrode surface, water molecules undergo a reduction reaction under certain potential conditions, producing hydrogen gas. This process not only consumes the active materials in the battery, leading to a decrease in energy conversion efficiency, but also causes severe corrosion to the zinc negative electrode. As the HER continues, the zinc negative electrode surface is continuously eroded, gradually reducing the effective surface area of ​​the electrode and increasing resistance, further affecting the battery's charge and discharge performance. Simultaneously, the hydrogen gas produced by the HER forms bubbles on the electrode surface. These bubbles interfere with the uniform deposition of zinc ions on the electrode surface, promoting the growth of zinc dendrites. Once formed, zinc dendrites grow like sharp "ice picks" inside the battery, further exacerbating electrode corrosion and potentially piercing the battery separator, causing a short circuit between the positive and negative electrodes, leading to battery failure, and seriously jeopardizing the battery's safety performance and lifespan. Therefore, suppressing the HER reaction is crucial for the development of aqueous zinc batteries. Only by effectively solving the HER problem can the stability of the zinc anode be improved, the self-discharge rate of the battery be reduced, and the coulombic efficiency of the battery be increased. This will enable the battery to achieve long cycle life and high energy conversion efficiency, promoting the aqueous zinc battery from the laboratory to widespread practical applications and truly realizing its enormous potential in the future energy storage field.

[0003] HER is a typical interfacial electrochemical side reaction in which water molecules are reduced to hydrogen gas on the zinc anode surface. This not only reduces battery efficiency but also leads to anode corrosion, surface passivation, and even hydrogen evolution bubbles blocking active sites, thereby accelerating zinc dendrite growth and shortening battery life. To gain a deeper understanding of the HER mechanism, existing studies have used density functional theory (DFT) to simulate the adsorption behavior and reaction pathways on the electrode surface, or used micro-kinetic models to analyze electrode reaction kinetics. However, these methods are mostly limited to static models or single reaction pathways, failing to fully consider the coupling effects between interfacial electric field, electrolyte mass transfer, charge rearrangement, and reaction conditions (such as pH, potential, and temperature). This results in a lack of accurate prediction and dynamic quantification of H-ion anode coverage during the actual reaction process, limiting its practical guiding significance in HER regulation and zinc battery structural design.

[0004] In summary, existing technologies suffer from the drawback of being unable to accurately predict the coverage of H ions on the Zn anode surface at multiple scales, and lacking a holistic modeling method that can couple electronic structure, reaction kinetics, and macroscopic mass transfer processes. Summary of the Invention

[0005] To address the shortcomings of existing technologies, such as the difficulty in accurately predicting the coverage of H ions on the Zn anode surface at multiple scales and the lack of a holistic modeling method that couples electronic structure, reaction kinetics, and macroscopic mass transfer processes, the technical solution provided by this invention is as follows: A method for predicting H-ion negative electrode coverage in a Zn aqueous battery includes: The steps include: constructing a three-dimensional atomic structure model of H atoms and the adsorption surface of Zn electrodes, performing geometric optimization, calculating the formation energy and adsorption energy of H ions and water molecules, and establishing the reaction potential energy surface. The steps involve importing formation energy, adsorption energy, transition state energy barrier and vibrational frequency data into a micro-kinetic-electrochemical model, constructing reaction pathways and electron transfer behavior, defining potential-related parameters, and outputting reaction rate, current density and coverage data. Based on the reaction rate and current density, the corresponding geometric structure and boundary conditions are constructed, and a phase field dilute mass transfer model including temperature, pH and pressure parameters is established to complete the steps of mass migration and charge distribution calculation. The steps of feeding back the interface physical field parameters to the microscopic dynamic model, iteratively updating the reaction rate and the state of active sites, forming a two-way data interaction between micro and macro and achieving self-consistent convergence; The steps involve outputting the coupled model results, obtaining multi-scale prediction data, and quantitatively analyzing the negative electrode coverage behavior of H ions in Zn aqueous batteries.

[0006] Furthermore, a preferred embodiment is provided in which the molecular structure visualization tool used in the three-dimensional atomic structure model is selected from GaussView, Materials Studio, Chem3D, VMD, or CYLview.

[0007] Furthermore, a preferred embodiment is provided, in which the density functional theory calculation software is selected from Gaussian, VASP, ORCA, Quantum ESPRESSO or CP2K in calculating the formation energy and adsorption energy of H ions and water molecules.

[0008] Furthermore, a preferred embodiment is provided in which the reaction pathway included in the microkinetic-electrochemical model involves an electrocatalytic hydrogen evolution reaction.

[0009] Furthermore, a preferred implementation method is provided, in which the phase field rare matter transport model is discretized using a finite element mesh generation method.

[0010] Furthermore, a preferred implementation method is provided in which bidirectional data interaction between the microscopic dynamics model and the phase field rare matter transport model is achieved through an iterative algorithm.

[0011] A device for predicting the H-ion negative electrode coverage in a Zn aqueous battery, comprising: A three-dimensional atomic structure model of H atoms and Zn electrode adsorption surface was constructed, and geometric optimization was performed. The formation energy and adsorption energy of H ions and water molecules were calculated, and a module of reaction potential energy surface was established. This module imports data on formation energy, adsorption energy, transition state energy barrier, and vibrational frequency into a micro-kinetic-electrochemical model to construct reaction pathways and electron transfer behavior, define potential-related parameters, and output reaction rate, current density, and coverage data. Based on the reaction rate, a corresponding geometric structure and boundary conditions are constructed, and a phase-field dilute mass transfer model including temperature, pH and pressure parameters is established to complete the module for calculating mass migration and charge distribution. The interface physical field parameters are fed back to the microscopic dynamic model to iteratively update the reaction rate and the state of active sites, forming a module for two-way data interaction between micro and macro and achieving self-consistent convergence. This module outputs coupled model results, obtains multi-scale prediction data, and quantitatively analyzes the negative electrode coverage behavior of H ions in Zn aqueous batteries.

[0012] A computer storage medium for storing a computer program, which, when read by a computer, is used by the computer to execute the method described thereon.

[0013] A computer, including a processor and a storage medium, executes the method when the processor reads a computer program stored in the storage medium.

[0014] A computer program product, which, as a computer program, implements the method when the computer program is executed.

[0015] Compared with the prior art, the advantages of the technical solution provided by the present invention are as follows: This method employs density functional theory (DFT) in one step to calculate the formation energy of H ions and H₂O molecules, constructing a reaction potential energy surface. This allows parameters such as key energy barriers and adsorption free energy of the catalytic reaction to be obtained with atomic-scale precision, significantly improving the theoretical analytical capability for the reaction process at active sites. Compared to traditional thermodynamic simulation methods that rely on empirical parameters, this method can more realistically reflect the behavioral basis of H ions at the electrode interface, effectively improving the accuracy of activity description.

[0016] This approach, in one step, constructs a microscopic kinetic-electrochemical model, incorporating DFT calculations as input to establish a complete reaction network covering reaction pathways, electron transfer processes, and ambient potential. This allows for dynamic adjustment of parameters such as electrode reaction rate constants and current densities while considering potential dependence, significantly enhancing the kinetic simulation's responsiveness to actual electrochemical conditions. Compared to traditional microscopic models that only consider a single reaction pathway, this approach is more universal and adaptable.

[0017] In one step, a mathematical model of dilute mass transfer is established using phase-field software, defining reaction environment parameters such as temperature, pressure, and pH. Simultaneously, high-precision simulation of interfacial mass transfer behavior is achieved through geometric modeling and mesh generation, thus resolving the distortion issues caused by the simplified treatment of mass diffusion and concentration gradient distribution in traditional models. Compared to empirical models that ignore the effects of reaction interface structure, this approach can more accurately predict the impact of electrolyte concentration changes on the reaction rate.

[0018] In one step, a two-way data interaction mechanism is constructed through multi-scale coupling of the microscopic dynamics model and the phase-field model. This enables real-time transmission and self-consistent iteration of cross-scale information such as charge density, pH gradient, and current density, effectively bridging the modeling gap between microscopic reaction processes and macroscopic mass transfer behavior. Compared with traditional independent models that cannot achieve "quantum-mesoscopic-macroscopic" linkage, this scheme achieves, for the first time, the collaborative solution of complex electrode / electrolyte interface systems, significantly improving the reliability and application value of the prediction results.

[0019] This approach proposes a descriptor-driven microkinetic analysis framework to standardize and automate the quantification process from descriptor space to reaction rate. It overcomes the limitations of traditional microkinetic models, which are confined to specific reaction systems or static descriptors, and can be widely applied to catalyst screening, reaction condition optimization, and systematic analysis of common reaction mechanisms in heterogeneous catalysis. Compared to material screening methods that rely on empirically fitted parameters, this descriptor possesses greater adaptability and theoretical explanatory power, providing more physically-based criteria for catalyst design.

[0020] It is applicable to the identification of active sites and performance prediction in electrocatalytic reactions, especially in the quantitative analysis and regulation of H-ion negative electrode coverage in aqueous zinc batteries. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method for predicting the H ion coverage in the negative electrode of a Zn aqueous battery.

[0022] Figure 2 This represents the coverage of H atoms during the reduction reaction on the Zn electrode surface to generate H2. Detailed Implementation

[0023] To make the advantages and benefits of the technical solution provided by the present invention clearer, the technical solution provided by the present invention will now be described in further detail with reference to the accompanying drawings, specifically: Implementation Method 1: This implementation method provides a method for predicting the H-ion negative electrode coverage in a Zn aqueous battery, including: The steps include: constructing a three-dimensional atomic structure model of H atoms and the adsorption surface of Zn electrodes, performing geometric optimization, calculating the formation energy and adsorption energy of H ions and water molecules, and establishing the reaction potential energy surface. The steps involve importing formation energy, adsorption energy, transition state energy barrier and vibrational frequency data into a micro-kinetic-electrochemical model, constructing reaction pathways and electron transfer behavior, defining potential-related parameters, and outputting reaction rate, current density and coverage data. Based on the current density and coverage results, the corresponding geometric structure and boundary conditions are constructed, and a phase field dilute mass transfer model including temperature, pH and pressure parameters is established to complete the steps of mass migration and charge distribution calculation. The steps of feeding back the interface physical field parameters to the microscopic dynamic model, iteratively updating the reaction rate and the state of active sites, forming a two-way data interaction between micro and macro and achieving self-consistent convergence; The steps involve outputting the coupled model results, obtaining multi-scale prediction data, and quantitatively analyzing the negative electrode coverage behavior of H ions in Zn aqueous batteries.

[0024] In the three-dimensional atomic structure model, the molecular structure visualization tools used are selected from GaussView, MaterialsStudio, Chem3D, VMD, or CYLview.

[0025] For calculating the formation and adsorption energies of H ions and water molecules, density functional theory calculation software was selected from Gaussian, VASP, ORCA, Quantum ESPRESSO, or CP2K.

[0026] The reaction pathways included in the microkinetic-electrochemical model involve electrocatalytic hydrogen evolution reactions.

[0027] The phase field rare matter transport model is discretized using the finite element mesh generation method.

[0028] The bidirectional data interaction between the microscopic dynamics model and the phase field rare matter transport model is achieved through an iterative algorithm.

[0029] Implementation Method Two: This implementation method is a further detailed description of the technical solution provided in Implementation Method One, specifically: A method for predicting H-ion coverage at the anode in a Zn aqueous battery combines density functional theory (DFT), microdynamic simulation, and phase field simulation. Through multi-scale coupled modeling, it achieves accurate quantification and coverage prediction of H-ion behavior at the Zn anode interface.

[0030] First, the reaction potential surface is constructed.

[0031] In this step, a three-dimensional atomic structure model of H atoms and adsorption surfaces is constructed using molecular structure visualization software. The modeling tool is used to draw the H atom model involved in the reaction, and the key adsorption plane structure is established in the visualization interface. After completing the three-dimensional geometric model, density functional theory is used to optimize the geometric structure of the model to obtain the configuration with the lowest energy. Next, H atoms are adsorbed onto the surface of the adsorption plane model to form an H adsorption model, which is then geometrically optimized again to obtain the stable structure of the system in the adsorption state. Based on the optimization results, the formation energy and adsorption energy of H ions and water molecules on the electrode surface are further calculated using the DFT method, thereby constructing a complete reaction potential energy surface and outputting the intermediate adsorption free energy, transition state energy barrier, and frequency data, providing input parameters for subsequent microscopic dynamics modeling.

[0032] Next, a micro-kinetic-electrochemical model was established.

[0033] The structured data, including the adsorption free energy, energy barrier, and frequency of intermediates obtained in the first step, were imported into a microscopic kinetic simulation software to construct a reaction network centered on the electrode reaction pathway. For the electron transfer reaction steps, the electronic participation behavior of reactants, products, and intermediates was clarified, and the elementary reaction mechanism was defined. Based on the reaction environment, potential-related parameters such as potential and electrolyte composition were set, and a potential-dependent reaction kinetic expression was constructed, with a convergence criterion for the simulation established. The model outputs the rate constants, current densities, and active site coverage trends for each step in the reaction process, providing boundary conditions for the next step of coupling macroscopic mass transfer behavior.

[0034] Then, a phase-field model for rare matter transport is established.

[0035] In this step, the geometry of the reaction system is modeled using phase-field software, defining the boundary shape, thickness, and dimensional parameters of the electrode / electrolyte interface, and setting environmental factors such as temperature, pressure, and pH within the simulation region. A physical field for dilute mass transfer is established, including modeling the diffusion, convection, and migration processes of major reactants such as Zn²⁺ and H⁺, as well as byproducts. By setting appropriate boundary conditions and mass transfer coupling terms, the model can reflect the local concentration gradient and charge distribution at the electrode interface caused by the electrochemical reaction. Finally, the simulation mesh is optimized to ensure sufficient resolution in the interface region, thereby guaranteeing simulation accuracy and numerical convergence.

[0036] Furthermore, multi-scale coupling of the micro-dynamic model and the phase field model is implemented.

[0037] The reaction rate and current density calculated in the micro-kinetic model are passed as boundary input conditions to the phase-field model. Simultaneously, the charge density, local pH gradient, and other interfacial physical field information output by the phase-field model are fed back to the micro-model to update the reaction rate and potential distribution. This two-way coupling mechanism constructs a self-consistent iterative computational framework, iterating until the key parameters of both models converge, thus obtaining a stable description of the reaction behavior. The final output of this step is a multi-scale dataset containing parameters such as coverage, charge density, partial current density, and Faraday efficiency, which can be used to evaluate electrode material performance, optimize interfacial structure design, and provide theoretical support for subsequent catalyst development.

[0038] Implementation Method 3: Combination Figure 1-2 This embodiment describes the technical solution provided above in further detail through specific examples. Specifically: Zinc-ion batteries possess immense commercial potential due to their high theoretical capacity and ideal electrochemical redox potential. However, side reactions (HERs) at the zinc anode interface hinder the long-term operation of zinc-ion batteries, limiting their large-scale application. Therefore, obtaining data on the H coverage of the Zn surface is crucial. This embodiment addresses the challenges of dynamic identification of catalyst active sites and low performance prediction accuracy in heterogeneous catalytic reactions by proposing an activity quantification algorithm based on the coupling of the dynamic reaction environment. Compared to traditional catalytic simulation programs that rely solely on static crystal structures, energy calculation tools for single reaction pathways, and activity evaluation software that ignores interfacial interactions, the activity quantification algorithm proposed in this embodiment directly integrates the dynamic correlation between reaction conditions (temperature, pressure), interfacial interactions, and the electronic states of active sites. This enables precise localization of active sites and efficient prediction of catalyst performance in complex catalytic systems. The method for identifying active sites and predicting the performance of heterogeneous catalysts includes the following steps: (1) using the DFT method to calculate the formation energies of H ions and H2O molecules on the electrode reaction and establishing the reaction potential surface; (2) constructing a micro-kinetic-electrochemical model in micro-kinetic software; (3) constructing the input file in the micro-kinetic model; (4) establishing a mathematical model of dilute mass transfer based on phase field software, defining environmental parameters such as temperature, pressure, and pH of the reaction system and optimizing the mesh division; (5) realizing multi-scale coupling between the micro-kinetic model and the phase field software, and transferring micro-kinetic and interface physical field information through self-consistent cycling. This implementation method achieves rapid and accurate identification of active sites and efficient prediction of performance in complex heterogeneous catalytic systems by only using the coupling scale of dynamic reaction environment and electronic state of active sites. This program can be widely applied to various heterogeneous catalytic reactions synergistically regulated by interface electron transfer and reaction conditions.

[0039] A method for predicting the H-ion negative electrode coverage in a Zn aqueous battery is implemented according to the following steps: S1. The DFT method is used to calculate the formation energies of H ions and H2O molecules on the electrode reaction, and the reaction potential energy surface is established. S2. Construct a microdynamic-electrochemical model in microdynamic software; S3. Constructing the input file for the microscopic dynamics model; S4. Establish a mathematical model for rare mass transfer based on phase field software, define environmental parameters such as temperature, pressure, and pH value of the reaction system, and perform mesh generation and optimization. S5. Achieve multi-scale coupling between the microscopic dynamics model and the phase field software, and transmit microscopic dynamics and interface physical field information through self-consistent loops.

[0040] The types of catalytic reactions mentioned include electrocatalytic hydrogen evolution (HER), electrocatalytic oxygen evolution (OER), electrocatalytic CO2 reduction (CO2RR), electrocatalytic nitrogen reduction (NRR), heterogeneous catalytic dry reforming of methane, ammonia decomposition reaction, selective reduction of NOx in automobile exhaust (SCR), and oxygen reduction reaction at the cathode of fuel cell (ORR).

[0041] Step S1 specifically involves: S11. Use molecular structure visualization software to draw the H atom model; S12. Use the aforementioned molecular structure visualization software to convert the reaction intermediate model into a three-dimensional atomic coordinate file; S13. Optimize the geometric structure of the constructed H-atom model using density functional theory; S14. Use molecular structure visualization software to draw a model of the key adsorption plane; S15. Use the aforementioned molecular structure visualization software to convert the adsorption plane model into a three-dimensional atomic coordinate file; S16. Optimize the geometric structure of the constructed adsorption plane model using density functional theory; S17. Using the aforementioned molecular structure visualization software, convert the model of H atoms adsorbed on the plane into a three-dimensional atomic coordinate file. S18. The geometric structure of the constructed model of H atoms adsorbed in the plane is optimized using density functional theory.

[0042] The molecular structure visualization software mentioned is selected from GaussView, Materials Studio, Chem3D, VMD, and CYLview.

[0043] The density functional theory calculation software mentioned is selected from Gaussian, VASP, ORCA, Quantum ESPRESSO (QE), and CP2K.

[0044] The methods for constructing models of intermediates adsorbed on a plane in the reaction are selected from: manual docking using molecular structure visualization software, automatic docking using molecular docking software, covalent modification based on crystal plane structure, and adsorption site search using surface periodic models.

[0045] Step S2 specifically involves: S21. Data on the adsorption free energy, transition state energy barrier, and vibrational frequency of key intermediates obtained through density functional theory (DFT) calculations are imported into the microscopic dynamics model in a structured text format. S22, for elementary reactions involving electron transfer; S23. Construct environmental parameters and potential parameters for the reaction to occur.

[0046] Step S3 specifically involves: S31. The influence of quantified adsorption species interactions on kinetics; S32. Define the convergence criteria for the microdynamic-electrochemical model.

[0047] Step S4 specifically involves: S41. Perform geometric modeling and model definition in the phase-field model; S42. Complete the setting of the physical field for rare matter transfer in the phase field model; S43. Complete the boundary condition settings for the phase field model; S43. Complete the mesh generation optimization settings for the phase field model.

[0048] Step S5 specifically involves: S51, Microdynamics - Data interaction during the transmission process: the microdynamics software transmits boundary conditions to the phase field software, and the phase field software transmits the double layer values ​​to the microdynamics model. S52. The current density is calculated by outputting the results of the micro-dynamic model to complete the self-consistent cycle and convergence control. S53. The final output of the coupled model includes a multi-scale dataset containing microscopic dynamic parameters (activation energy, coverage), interface physical fields (charge density, pH gradient), and macroscopic performance (partial current density, Faraday efficiency), which is used to analyze the limiting mechanism of the double layer on the reaction rate.

[0049] (1) This embodiment addresses the key scientific and engineering challenges of how mass transfer processes significantly affect electrode reaction rates by providing an accurate and rapid prediction method based on multi-scale coupling. In electrochemical reactions (as described in claim 2), especially under conditions of high reaction rates or low reactant concentrations, mass transport at the electrode / electrolyte interface often becomes the limiting step, resulting in actual measured current densities that are far lower than theoretical values ​​predicted solely based on intrinsic kinetics at the electrode surface. Traditional methods struggle to efficiently and quantitatively reveal the complex coupling effects between microscopic interfacial reaction kinetics and macroscopic mass transfer processes, and their impact on the overall reaction rate.

[0050] (2) This implementation realizes real-time bidirectional data interaction between the microscopic dynamics model and the phase field software, and constructs the first fully coupled "quantum-microscopic dynamics-continuous medium" model. Traditional electrochemical models (such as the Butler-Volmer equation) rely on empirical transfer coefficients and cannot analyze the nonlinear effects of the dynamic changes in the double layer structure on the reaction kinetics; while first-principles calculations can describe the atomic-scale electronic structure, they are difficult to couple with macroscopic environmental parameters.

[0051] (3) The catalytic activity descriptor proposed in this embodiment is a universal descriptor that uniformly quantifies the interfacial electric field effect of heterogeneous catalytic reactions: electron transfer between the catalyst and reactants and intermediates is the common scientific essence of various catalytic elementary reactions. In particular, the transfer of electrons from the catalyst to the antibonding orbitals of reactant molecules is a common key step in initiating reactions in many catalytic processes. Traditional descriptors (such as d-band centers and oxygen p-band centers) are only applicable to specific material systems (metals / metal oxides) and cannot be compatible with the double-layer equivalent mechanism of electrocatalysis and thermocatalysis.

[0052] The above description of several specific embodiments further details the technical solution provided by the present invention in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, combinations of embodiments, and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the H-ion negative electrode coverage in a Zn aqueous battery, characterized in that, include: The steps include: constructing a three-dimensional atomic structure model of H atoms and the adsorption surface of Zn electrodes, performing geometric optimization, calculating the formation energy and adsorption energy of H ions and water molecules, and establishing the reaction potential energy surface. The steps involve importing formation energy, adsorption energy, transition state energy barrier and vibrational frequency data into a micro-kinetic-electrochemical model, constructing reaction pathways and electron transfer behavior, defining potential-related parameters, and outputting reaction rate and coverage data. Based on the reaction rate and current density, the corresponding geometric structure and boundary conditions are constructed, and a phase field dilute mass transfer model including temperature, pH and pressure parameters is established to complete the steps of mass migration and charge distribution calculation. The steps of feeding back the interface physical field parameters to the microscopic dynamic model, iteratively updating the reaction rate and the state of active sites, forming a two-way data interaction between micro and macro and achieving self-consistent convergence; The steps involve outputting the coupled model results, obtaining multi-scale prediction data, and quantitatively analyzing the negative electrode coverage behavior of H ions in Zn aqueous batteries.

2. The method for predicting the H-ion negative electrode coverage in a Zn aqueous battery according to claim 1, characterized in that, In the three-dimensional atomic structure model, the molecular structure visualization tools used are selected from GaussView, Materials Studio, Chem3D, VMD, or CYLview.

3. The method for predicting the H-ion negative electrode coverage in a Zn aqueous battery according to claim 1, characterized in that, For calculating the formation and adsorption energies of H ions and water molecules, density functional theory calculation software was selected from Gaussian, VASP, ORCA, Quantum ESPRESSO, or CP2K.

4. The method for predicting the H-ion negative electrode coverage in a Zn aqueous battery according to claim 1, characterized in that, The reaction pathways included in the microkinetic-electrochemical model involve electrocatalytic hydrogen evolution reactions.

5. The method for predicting the H-ion negative electrode coverage in a Zn aqueous battery according to claim 1, characterized in that, The phase field rare matter transport model is discretized using the finite element mesh generation method.

6. The method for predicting the H-ion negative electrode coverage in a Zn aqueous battery according to claim 1, characterized in that, The bidirectional data interaction between the microscopic dynamics model and the phase field rare matter transport model is achieved through an iterative algorithm.

7. A device for predicting the H-ion negative electrode coverage in a Zn aqueous battery, characterized in that, include: A three-dimensional atomic structure model of H atoms and Zn electrode adsorption surface was constructed, and geometric optimization was performed. The formation energy and adsorption energy of H ions and water molecules were calculated, and a module of reaction potential energy surface was established. The module imports data on formation energy, adsorption energy, transition state energy barrier and vibrational frequency into a micro-kinetic-electrochemical model to construct reaction pathways and electron transfer behavior, define potential-related parameters, and output reaction rate and coverage data. Based on the reaction rate, a corresponding geometric structure and boundary conditions are constructed, and a phase-field dilute mass transfer model including temperature, pH and pressure parameters is established to complete the module for calculating mass migration and charge distribution. The interface physical field parameters are fed back to the microscopic dynamic model to iteratively update the reaction rate and the state of active sites, forming a module for two-way data interaction between micro and macro and achieving self-consistent convergence. This module outputs coupled model results, obtains multi-scale prediction data, and quantitatively analyzes the negative electrode coverage behavior of H ions in Zn aqueous batteries.

8. A computer storage medium for storing computer programs, characterized in that, When the computer program is read by the computer, the computer executes the method of claim 1.

9. A computer, comprising a processor and a storage medium, characterized in that, When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1.

10. A computer program product, as a computer program, is characterized by: When the computer program is executed, it implements the method of claim 1.

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