Method for researching corrosion resistance behavior of zinc alloy based on first principle

Abstract

The invention discloses a method for researching the corrosion resistance behavior of zinc alloy based on a first principle. The method comprises the following steps: firstly, retrieving a zinc primitive cell model from a material database and carrying out structure optimization; performing crystal face cutting, cell expansion and re-structure optimization to obtain a zinc plate model; then aluminum atom random replacement doping processing is carried out, and optimization is carried out to obtain a doping model; performing static self-consistent calculation to obtain surface energy, and screening out the most stable doping model; fixing two layers of atoms at the bottom of the most stable doping model, performing chloride ion adsorption calculation and structure optimization, and calculating adsorption energy to determine a most stable adsorption site; and finally, carrying out electronic structure calculation of state density and a work function, and representing the corrosion resistance of the zinc alloy. According to the method, a zinc alloy corrosion micromechanism is deeply analyzed, theoretical guidance is provided for component optimization and corrosion resistance improvement, and compared with a traditional experimental method, the research and development cost and time are remarkably reduced, the research efficiency is improved, and the method has important theoretical and practical application value.

Description

Technical Field

[0001] This invention relates to the technical field of zinc alloys, and more particularly to a method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations. Background Technology

[0002] Zinc alloys are widely used in automotive parts, building structures, and electronic device housings due to their low cost and excellent processability. However, in actual service, zinc alloys are quite sensitive to corrosive environments, especially in media containing chloride ions, water vapor, or acids and alkalis, where they are prone to electrochemical corrosion reactions. This not only affects their service life but also reduces structural reliability. To improve the corrosion resistance of zinc alloys, research often involves alloying with alloying elements such as aluminum and magnesium into the zinc matrix. Among these, aluminum is one of the most commonly used corrosion-enhancing elements. It can form a stable oxide film on the alloy surface, thereby blocking the penetration and diffusion of corrosive media to a certain extent and improving the corrosion behavior of zinc alloys in chloride environments.

[0003] Nevertheless, existing technologies still have some limitations. On the one hand, while increasing the aluminum content can improve the density of the passivation film, excessively high aluminum content can lead to alloy embrittlement and decreased ductility, thus affecting the material's processing performance and service reliability. Therefore, finding the optimal alloy doping ratio is crucial. On the other hand, most existing corrosion resistance studies rely on experimental methods, such as electrochemical testing, salt spray testing, or natural exposure testing. While these methods can assess material performance at the macroscopic level, they suffer from long testing cycles, high costs, and significant susceptibility to environmental interference, making it difficult to accurately analyze the microscopic mechanisms of different elements in the alloy on the corrosion process. Furthermore, there is currently a lack of methods to quantitatively assess changes in alloy composition and predict its performance in corrosive environments during the material design phase. For example, application number CN202411479729.1 only focuses on the adsorption process of the corrosive medium, failing to further analyze the charge transfer, barrier changes, and product stability of the corrosion reaction at the electronic structure level, thus failing to quantitatively characterize corrosion kinetics.

[0004] Therefore, there is an urgent need to develop a theoretically computationally driven research approach to systematically analyze the influence of different aluminum contents on the electronic structure and corrosion behavior of zinc-aluminum alloys, thereby providing a theoretical basis for optimizing the material composition and improving the corrosion resistance of zinc alloys. This invention, based on first-principles calculations, studies the surface reaction processes, electronic structure characteristics, and adsorption behavior of Zn-4Al and Zn-6Al alloys in corrosive environments. The aim is to construct a microscopic corrosion mechanism model under aluminum content regulation, solving the prediction and mechanism identification problems that are difficult to achieve with traditional experimental methods, and providing theoretical support for the application of zinc-aluminum alloys in complex service environments. Summary of the Invention

[0005] In view of the many shortcomings of existing technologies in the study of zinc alloy corrosion behavior, this invention proposes a first-principles method for studying the corrosion resistance of zinc alloys. This method aims to solve the problems of lengthy experimental cycles, high costs, and susceptibility to environmental interference faced by traditional experimental methods. It also overcomes the limitations of existing theoretical models in simulating the corrosion behavior of zinc-aluminum alloys, such as the limited model selection, lack of consideration for surface evolution mechanisms, and the difficulty in accurately analyzing the microscopic roles of different elements in the corrosion process.

[0006] In summary, the inventive concept of this application lies in providing a method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations. This method first obtains a hexagonal close-packed zinc unit cell model from a materials database; then, it performs crystal plane cutting, cell expansion, and structural optimization to obtain a zinc plate model; next, it introduces aluminum elements into the model for random doping in different proportions to construct a representative zinc-aluminum alloy surface structure. By calculating the surface energy of different crystal planes, the most thermodynamically stable crystal plane is selected. Based on this, corrosion-related ions (such as chloride ions) are introduced to simulate adsorption at multiple adsorption sites. The most corrosion-prone sites are selected by calculating the adsorption energy. Furthermore, electronic structure indicators such as density of states and work function are combined to reveal the corrosion mechanism in depth. This invention, by combining surface physics, alloy design, and corrosion chemistry, achieves integrated prediction of multi-scale information. Compared with traditional experimental corrosion performance evaluation methods, it significantly improves screening efficiency, reduces experimental costs, and provides theoretical support and quantitative basis for the design and optimization of high-performance corrosion-resistant zinc-aluminum alloys.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations, the method comprising the following steps:

[0009] S1: Retrieve zinc unit cell models from the materials database and optimize their structures;

[0010] S2: Perform crystal plane cutting, cell expansion, and structural optimization on the zinc unit cell model optimized in step S1 to obtain the zinc plate model;

[0011] S3: Perform random aluminum atom replacement doping on the zinc plate model obtained in step S2 to obtain a doped model, and optimize the structure of the doped model.

[0012] S4: Perform static self-consistent calculation of surface energy on the doped model obtained in step S3, and select the doped model with the most stable surface.

[0013] S5: Fix the bottom two layers of atoms of the doped model obtained in step S4, and then perform adsorption calculations for different chloride ion adsorption sites and optimize the structure.

[0014] S6: Calculate the adsorption energy of different adsorption sites in the doping model obtained in step S5 to obtain the most stable adsorption site.

[0015] S7: Perform electronic structure calculations, including density of states and work function, on the most stable adsorption sites obtained in step S6 to characterize the corrosion resistance of the zinc alloy.

[0016] This invention, through a series of first-principles calculations—including retrieving zinc unit cell models from material databases, crystal plane cutting and expansion, aluminum atom doping, surface energy calculation to screen stable surfaces, chloride ion adsorption calculations, and electronic structure calculations—enables in-depth analysis of the microscopic behavior of zinc alloys in corrosive environments. This includes the chloride ion adsorption process and changes in electronic structure, leading to a more accurate understanding of the intrinsic mechanisms of corrosion. Simultaneously, based on the calculated adsorption energy and surface energy parameters, the corrosion resistance of zinc alloys with different aluminum contents can be predicted, providing a theoretical basis for optimizing the composition of zinc alloys and facilitating the design of zinc-aluminum alloys with superior corrosion resistance. Furthermore, compared to traditional experimental methods, first-principles calculations eliminate the need for extensive experimental trial and error, significantly reducing the number of experiments, shortening the development cycle, and lowering development costs. Additionally, step S5, which fixes the bottom two layers of atoms in the doped model, aims to more realistically simulate physical conditions, improve computational efficiency, maintain model stability, and simplify boundary condition handling, thereby obtaining more accurate and meaningful simulation results.

[0017] As a preferred technical solution, the structural optimization includes optimizing the models obtained in steps S1, S2, S3, and S5 respectively, generating the corresponding KPOINTS and POTCAR files required for model optimization and calculation using the VASPKIT software package, obtaining the CONTCAR file through structural optimization, and extracting the lattice parameters of the model from the CONTCAR file using VESTA software. This invention, by using the VASPKIT software package to generate the KPOINTS and POTCAR files required for calculation, and by obtaining the CONTCAR file through structural optimization and extracting the geometric information of the model using VESTA software, ensures the accuracy and consistency of calculation parameters and improves the reliability of calculation results. Simultaneously, the use of VASPKIT and VESTA software makes the calculation process more automated and convenient, reduces operational difficulty, improves work efficiency, and allows researchers to focus more on the research itself rather than tedious calculation operations.

[0018] As a preferred technical solution, the zinc unit cell model is a hexagonal close-packed structure containing two zinc atoms. This invention limits the zinc unit cell model to a hexagonal close-packed structure containing two zinc atoms, making the research more targeted and enabling a more accurate simulation of actual zinc alloy materials. This provides an accurate basic model for subsequent studies on doping and corrosion behavior. Furthermore, choosing this relatively simple model helps reduce computational complexity and improve computational efficiency, allowing the research to be completed within reasonable computational resources and time.

[0019] As a preferred technical solution, in step S2, the crystal plane models obtained by crystal plane cutting include (100), (110), and (101) crystal plane models. This invention, by studying zinc plate models with different crystal planes (including (100), (110), and (101) crystal planes), can comprehensively evaluate the behavioral differences of different crystal planes during corrosion, contributing to a deeper understanding of the influence of crystal plane orientation on the corrosion resistance of zinc alloys and providing theoretical support for selecting appropriate crystal plane orientations in practical applications. Simultaneously, different crystal planes may have different probabilities of exposure in actual service environments. By studying the corrosion resistance behavior of these crystal planes, it is possible to better simulate and predict the corrosion of zinc alloys under actual service conditions, improving the practicality and guiding significance of the research.

[0020] As a preferred technical solution, in step S2, the cell expansion process includes adding a vacuum layer with a thickness of 20 Å onto the crystal plane model. This invention, by adding a 20 Å thick vacuum layer to the crystal plane model for cell expansion, effectively avoids interactions between different crystal planes, ensuring that each crystal plane model operates independently during calculation. This results in a more accurate reflection of the physicochemical properties of individual crystal planes and improves the accuracy of the calculation results. Furthermore, the vacuum layer simulates the environment of actual material surfaces in air or other media, making the calculated surface properties closer to reality and enhancing the practical significance of the research results.

[0021] As a preferred technical solution, in step S3, the random substitution doping treatment of aluminum atoms is carried out according to the zinc to aluminum mass ratio of 96:4 and 94:6. This invention, by doping aluminum atoms according to zinc to aluminum mass ratios of 96:4 and 94:6, can systematically analyze the influence of aluminum content on the corrosion resistance of zinc alloys, providing data support for determining the optimal aluminum doping ratio, thereby achieving effective control over the corrosion resistance of zinc alloys. Simultaneously, it covers two typical doping ratios, broadening the research scope and providing a more comprehensive reference for the composition design of zinc-aluminum alloys, contributing to the development of zinc alloy materials with different performance requirements.

[0022] As a preferred technical solution, in step S4, the surface energy calculation formula is:

[0023] ,

[0024] Among them, E slab The energy of the slab, E bulk Let A be the energy of the basic unit, n be the surface area, n be the number of atoms in the basic unit, and N be the number of atoms in the plate model. The lower the surface energy, the more thermodynamically stable the crystal facet, and the greater the likelihood of the crystal facet being exposed during actual service. This invention, by employing a specific surface energy calculation formula, can accurately screen for the most thermodynamically stable crystal facets. This is of great significance for predicting the crystal facets most likely to be exposed in zinc alloys under actual service conditions, and helps in further research on the corrosion resistance of these crystal facets. Furthermore, the calculation of surface energy provides a quantitative basis for assessing crystal facet stability, making the research results more objective and accurate, avoiding errors caused by subjective judgment, and enhancing the scientific rigor and persuasiveness of the research.

[0025] As a preferred technical solution, in step S5, the adsorption calculation of different chloride ion adsorption sites includes the adsorption calculation of top sites, bridge sites, and vacancy sites, which is used to comprehensively analyze the adsorption of chloride ions on the zinc alloy surface, thereby more accurately predicting the corrosion behavior of the zinc alloy.

[0026] As a preferred technical solution, in step S6, the formula for calculating the adsorption energy is: E ads =E Total -E slab -E adsorbate E Total E represents the total energy of the system containing the adsorbate. slab E represents the total energy on the surface of the zinc alloy matrix. adsorbate The adsorption energy represents the energy of an isolated chloride ion. A negative adsorption energy indicates that the adsorption process is exothermic, and the larger the absolute value of the adsorption energy, the higher the probability of adsorption. This invention, by employing a specific formula for calculating adsorption energy, can quantitatively calculate the adsorption energy of chloride ions at different adsorption sites. The magnitude of the adsorption energy directly reflects the adsorption stability of chloride ions at that site, thereby allowing for a quantitative assessment of the corrosion risk at different sites and providing a key quantitative indicator for evaluating the corrosion resistance of zinc alloys. Furthermore, by comparing the adsorption energy of zinc alloys with different doping ratios, it can be determined which doping ratio is more conducive to reducing the adsorption stability of chloride ions, thus guiding the optimization of zinc alloy composition to improve its corrosion resistance.

[0027] As a preferred technical solution, the electronic structure calculation further includes atomic or orbital segmentation of the density of states, solved using VASPKIT software. This invention, by segmenting the density of states into atoms or orbitals and solving it using VASPKIT software, enables a more detailed analysis of the contribution of different atoms or orbitals to the electronic structure. This allows for a deeper understanding of the electronic structure underlying the differences in corrosion resistance of zinc alloys with different doping ratios, providing a more profound perspective on understanding the microscopic mechanisms of corrosion resistance. Simultaneously, the use of VASPKIT software makes the density of states segmentation calculation more efficient and accurate, improving the precision of the research and helping to discover details that might be overlooked in conventional calculations, further enhancing the quality and depth of the research.

[0028] The beneficial effects of this invention are:

[0029] This invention presents a first-principles calculation method for studying the corrosion resistance behavior of zinc alloys. By innovatively combining materials database retrieval, polycrystalline model construction, aluminum atom doping, chloride ion adsorption simulation, and electronic structure calculation, it not only achieves an in-depth analysis of the microscopic mechanisms of zinc alloy corrosion resistance but also effectively predicts the stability of zinc alloys in corrosive environments with different aluminum contents. This design enhances the understanding of the zinc alloy corrosion process while maintaining flexibility in alloy composition optimization, thus guiding the material design of high-performance corrosion-resistant zinc-aluminum alloys. In summary, this first-principles calculation method not only provides excellent theoretical guidance but also significantly reduces the cost and time consumption of traditional experimental methods, meeting the long-term durability and stability requirements of zinc alloy materials in complex service environments. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the operation of the material design method for the corrosion-resistant zinc alloy of the present invention.

[0031] Figure 2 This refers to the zinc protocell model obtained in step S1 of this invention.

[0032] Figure 3 This is a schematic diagram of the adsorption sites of the present invention.

[0033] Figure 4 This is a schematic diagram of the crystal structure of the stable adsorption sites in the Zn-4Al doping model.

[0034] Figure 5 This is a schematic diagram of the crystal structure of the stable adsorption sites in the Zn-6Al doping model.

[0035] Figure 6 This is the density of states diagram of stable adsorption sites in the Zn-4Al doping model.

[0036] Figure 7This is the density of states diagram of stable adsorption sites in the Zn-6Al doping model.

[0037] Figure 8 The work function plot of the stable adsorption sites in the Zn-4Al doping model.

[0038] Figure 9 The work function plot of the stable adsorption sites in the Zn-6Al doping model. Detailed Implementation

[0039] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0040] Example 1

[0041] This embodiment is based on a first-principles study of the corrosion resistance behavior of zinc alloys, such as... Figure 1 As shown, the method includes the following steps:

[0042] S1: Retrieve zinc unit cell models from the materials database and optimize their structures;

[0043] Specifically, the required hexagonal close-packed (HCP) zinc unit cell model, containing two zinc atoms, was retrieved from the Materials Project database. Figure 2 As shown. The retrieved zinc unit cell model was structurally optimized using first-principles calculations. The basic physical information (lattice constants) of the crystal structure of the optimized zinc unit cell model are as follows: a = 2.61354 Å, b = 2.61354 Å, c = 4.80674 Å, α = 90°, β = 90°, γ = 120°.

[0044] S2: Perform crystal plane cutting, cell expansion, and structural optimization on the zinc unit cell model optimized in step S1 to obtain the zinc plate model;

[0045] Specifically, the zinc unit cell model optimized in step S1 was cut into crystal planes using Materials Studio to obtain (100), (110), and (101) crystal plane models. Cell expansion was performed on each crystal plane model, and a 20 Å thick vacuum layer was added to the model to avoid interactions between different crystal planes. After cell expansion, a zinc plate model containing 72 atoms was obtained, and its structure was optimized. The lattice constants of the optimized zinc plate model are shown in Table 1.

[0046] Table 1. Lattice constants after optimization of the zinc plate model

[0047]

[0048] S3: Perform random aluminum atom replacement doping on the zinc plate model obtained in step S2 to obtain a doped model, and optimize the structure of the doped model.

[0049] Specifically, stable zinc plate models were subjected to random aluminum atom substitution doping at zinc-aluminum mass ratios of 4:96 and 6:94, respectively, to obtain doped models (i.e., Zn-4Al and Zn-6Al zinc alloy doped models), and the structures of the doped models were optimized. The lattice constants of the optimized doped models are shown in Table 2.

[0050] Table 2. Lattice constants after doping model optimization

[0051]

[0052] S4: Perform static self-consistent calculation of surface energy on the doped model obtained in step S3, and select the doped model with the most stable surface.

[0053] Specifically, static self-consistent calculations were performed on the doping models. The surface energies of three different zinc plate models for Zn-4Al and Zn-6Al were calculated using the surface energy calculation formula. The doping model with the most stable surface was selected (here, "most stable surface" specifically refers to the most thermodynamically stable crystal face). Crystal faces with lower surface energy values ​​have lower formation energy per unit area, indicating greater thermodynamic stability. Therefore, they are more likely to preferentially grow or remain on the material surface and are the crystal faces most likely to be exposed in actual service environments. The surface energy calculation formula is as follows:

[0054] Among them, E slab The energy of the slab, E bulk Let A be the energy of the basic unit, n be the surface area, n be the number of atoms in the basic unit, and N be the number of atoms in the plate model. The calculation results are shown in Table 3.

[0055] Table 3 Surface Energy

[0056]

[0057] As shown in Table 3, the most stable surface is the (100) plane. This crystal plane is more thermodynamically stable and is more likely to grow or remain on the material surface.

[0058] S5: Fix the bottom two layers of atoms of the doped model obtained in step S4, and then perform adsorption calculations for different chloride ion adsorption sites and optimize the structure.

[0059] Specifically, the bottom two layers of atoms in the most stable doped model are fixed, and adsorption calculations are performed on chloride ion top sites (T1, T2), bridge sites (B1, B2), and vacancies (H1, H2), followed by structural optimization. Here, T1: directly above a zinc atom on the surface; T2: directly above an aluminum atom on the surface; B1: between two zinc atoms on the surface; B2: between a zinc atom and an aluminum atom on the surface; H1: the middle position of three zinc atoms on the surface; and H2: the middle position of two zinc atoms and one aluminum atom on the surface. A schematic diagram of the adsorption sites is shown below. Figure 3 As shown in Table 4, the basic physical information of the optimized crystal structure is as follows:

[0060] Table 4. Lattice constants of zinc alloy doping models after optimization for different adsorption sites.

[0061]

[0062] By performing chloride ion adsorption calculations on the surface of the doped model, the results show that chloride ions were successfully adsorbed on the surface of the doped model, and the model constructed in this embodiment is reasonable.

[0063] S6: Calculate the adsorption energy of different adsorption sites in the doping model obtained in step S5 to obtain the most stable adsorption site.

[0064] Specifically, self-consistent calculations were performed on zinc alloy doping models with different adsorption sites. The adsorption energies of different adsorption sites in Zn-4Al and Zn-6Al alloys were calculated using the adsorption energy calculation formula to obtain the most stable adsorption site (here, the most stable adsorption site refers to the one with a negative calculated adsorption energy value and the largest absolute value). The adsorption energy calculation formula is: E ads =E Total -E slab -E adsorbate E Total E represents the total energy of the system containing the adsorbate. slab E represents the total energy on the surface of the zinc alloy matrix. adsorbate The adsorption energy represents the energy of an isolated chloride ion. A negative adsorption energy indicates that the adsorption process is exothermic, and the larger the absolute value of the adsorption energy, the higher the probability of adsorption. The calculation results are shown in Table 5.

[0065] Table 5 Adsorption Energy

[0066]

[0067] As shown in Table 5, the most stable adsorption sites for Zn-4Al and Zn-6Al alloys are bridge site 2 (B2) and top site 2 (T2), respectively, as shown in Table 5. Figure 4 and Figure 5 As shown.

[0068] S7: Perform electronic structure calculations, including density of states and work function, on the most stable adsorption sites obtained in step S6 to characterize the corrosion resistance of the zinc alloy.

[0069] Specifically, electronic structure calculations, including density of states and work function, were performed on the most stable adsorption sites to characterize the corrosion resistance of the zinc alloy. A more pronounced orbital hybridization in the density of states indicates a higher probability of corrosion; a smaller work function indicates easier surface adsorption and thus easier corrosion. Calculation results show that the addition of aluminum reduces chloride ion adsorption, thereby improving the alloy's corrosion resistance. The specific results are shown in the figure below:

[0070] Density of states diagram: Figure 6 and Figure 7 The density of states diagrams for Zn-4Al and Zn-6Al alloys are shown respectively.

[0071] Work function graph: Figure 8 and Figure 9 The work function diagrams are for Zn-4Al and Zn-6Al alloys, respectively.

[0072] Through the above steps, this invention not only provides an in-depth analysis of the microscopic behavior of zinc alloys in corrosive environments, but also offers theoretical guidance for optimizing the composition and improving the corrosion resistance of zinc alloys. The calculation results of the electronic structure in this embodiment reveal the underlying reasons why Zn-6Al alloy exhibits superior corrosion resistance compared to Zn-4Al in corrosive environments.

[0073] In one preferred embodiment, the structural optimization includes optimizing the models obtained in steps S1, S2, S3 and S5 respectively, using the VASPKIT software package to generate the KPOINTS and POTCAR files required for the corresponding model optimization and calculation, obtaining the CONTCAR file through structural optimization, and using VESTA software to extract the geometric information of the model from the CONTCAR file, the geometric information including lattice constant, crystal plane angle, bond length and bond angle.

[0074] In one preferred embodiment, the electronic structure calculation further includes atomic or orbital partitioning of the density of states, which is solved using VASPKIT software.

[0075] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations, characterized in that, The method includes the following steps: S1: Retrieve zinc unit cell models from the materials database and optimize their structures; S2: Perform crystal plane cutting, cell expansion, and structural optimization on the zinc unit cell model optimized in step S1 to obtain the zinc plate model; S3: Perform random aluminum atom replacement doping on the zinc plate model obtained in step S2 to obtain a doped model, and optimize the structure of the doped model. S4: Perform static self-consistent calculation of surface energy on the doped model obtained in step S3, and select the doped model with the most stable surface. S5: Fix the bottom two layers of atoms of the doped model obtained in step S4, and then perform adsorption calculations for different chloride ion adsorption sites and optimize the structure. S6: Calculate the adsorption energy of different adsorption sites in the doping model obtained in step S5 to obtain the most stable adsorption site. S7: Perform electronic structure calculations, including density of states and work function, on the most stable adsorption sites obtained in step S6 to characterize the corrosion resistance of the zinc alloy.

2. The method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations as described in claim 1, characterized in that, The structural optimization includes optimizing the models obtained in steps S1, S2, S3 and S5 respectively, using the VASPKIT software package to generate the KPOINTS and POTCAR files required for the corresponding model optimization and calculation, obtaining the CONTCAR file through structural optimization, and using VESTA software to extract the lattice constants of the model from the CONTCAR file.

3. The method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations as described in claim 1, characterized in that, The zinc unit cell model is a hexagonal close-packed structure and contains two zinc atoms.

4. The method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations as described in claim 1, characterized in that, In step S2, the crystal plane model obtained by the crystal plane cutting includes (100), (110), and (101) crystal plane models.

5. The method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations as described in claim 1, characterized in that, In step S2, the cell expansion process includes adding a vacuum layer on the crystal plane model, the thickness of which is 20 Å.

6. The method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations as described in claim 1, characterized in that, In step S3, the random replacement doping treatment of aluminum atoms is carried out according to the mass ratio of zinc to aluminum of 96:4 and 94:

6.

7. The method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations as described in claim 1, characterized in that, In step S4, the surface energy calculation formula is as follows: ， Among them, E slab The energy of the slab, E bulk Let A be the energy of the basic unit, A be the surface area, n be the number of atoms in the basic unit, and N be the number of atoms in the plate model.

8. The method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations as described in claim 1, characterized in that, In step S5, the adsorption calculation of different chloride ion adsorption sites includes the adsorption calculation of top sites, bridge sites, and vacancy sites, which is used to comprehensively analyze the adsorption of chloride ions on the zinc alloy surface, thereby more accurately predicting the corrosion behavior of the zinc alloy.

9. The method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations as described in claim 1, characterized in that, In step S6, the adsorption energy is calculated using the formula: E ads =E Total -E slab -E adsorbate , of which E Total E represents the total energy of the system containing the adsorbate. slab E represents the total energy on the surface of the zinc alloy matrix. adsorbate The energy of an isolated chloride ion.

10. The method for studying the corrosion resistance behavior of zinc alloys based on first-principles calculations as described in claim 1, characterized in that, In step S7, the electronic structure calculation also includes atomic or orbital partitioning of the density of states, which is solved using VASPKIT software.

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