A method for predicting the occupancy of alloying elements in metal nanopores

Abstract

This invention relates to a method for predicting the occupancy of alloying elements in metallic nanopores, belonging to the field of metallic material defect prediction technology. The method includes the following steps: Simulated annealing to obtain a stable structure: obtaining the most stable structure of nanopores of different sizes through a simulated annealing model of stable metallic pore structures; Coordination number analysis and screening: analyzing the coordination distribution of the inner walls of the nanopores and classifying and screening the inner wall atoms according to the coordination number; DFT energy calculation and induction: using VASP software based on the DFT method to calculate the energy property parameters of candidate lattice points after being occupied by alloying elements, and summarizing the correlation between the energy property parameters and the coordination distribution information. This invention avoids the shortcomings of high-symmetry point selection methods and manual random selection methods, while significantly reducing the computational load of all calculation methods, exhibiting significant advantages in both efficiency and reliability.

Description

Technical Field

[0001] This invention relates to the field of metal material defect prediction technology, specifically to a method for predicting the location of alloying elements in metal nanopores. Background Technology

[0002] The formation of nanopores is a common phenomenon during the processing, preparation, and service of metallic materials. These nanopores often become aggregation sites for alloying elements, leading to the formation of precipitates and affecting the properties of the metallic materials. Therefore, studying the aggregation behavior of alloying elements at nanopores is of significant scientific and engineering importance for predicting and controlling material properties.

[0003] The study of the segregation behavior of alloying elements in metallic nanopores hinges on calculating the energy of different alloying elements at various lattice points on the inner wall of the nanopores. The main challenge is to select the most representative lattice points from numerous candidate points to reduce computational complexity.

[0004] Existing methods have certain limitations in the computational process. For example, the high-symmetry point selection method only considers points with high symmetry in the metal lattice, which may miss some stable sites with lower energy. The manual random selection method may require a large amount of computation to obtain reliable results, which is inefficient. The full computational method has a huge computational load, especially when the nanopores are large, making it difficult to implement.

[0005] Publication No. CN115910244B discloses a simulation method for determining the stable structure and energy of aluminum nanopores, which is the research result of the inventors. The method uses Wigner-Seitz unit cells to characterize the inner wall structure of nanopores and employs simulated annealing to calculate the surface area of ​​the Wigner-Seitz unit cells in aluminum nanopores. The nanopore with the smallest Wigner-Seitz unit cell surface area is the most stable structure, and its formation energy is directly proportional to the surface area of ​​the Wigner-Seitz unit cell. This method allows for the convenient and simple acquisition of the stable structure and energy of aluminum nanopores of any size.

[0006] Based on the above research results, this invention provides an efficient grid screening method to quickly model and screen out the stable positions of alloying elements within nanopores. Summary of the Invention

[0007] The purpose of this invention is to provide an accurate and simple method for predicting the location of alloying elements in metal nanopores, so as to solve the problems mentioned in the background art.

[0008] The present invention achieves the above objectives through the following technical solutions:

[0009] A method for predicting the occupancy of alloying elements in metal nanopores includes the following steps:

[0010] Step S1. Obtain a metal nanopore stable structure of arbitrary size according to the metal pore stable structure simulation annealing model, wherein the metal pore stable structure simulation annealing model is constructed based on a simulation method to determine the nanopore stable structure and energy in aluminum;

[0011] Step S2. Identify and label the atomic grid points on the inner wall of the pores in the metal nanopore stable structure, calculate the coordination number of each atomic grid point to generate coordination distribution information, classify the inner wall grid points according to the coordination distribution information and set classification conditions, and replace the alloy element atoms to be calculated on several classified inner wall grid points to obtain the pore-alloy element composite structure.

[0012] Step S3. Based on density functional theory, calculate the energy property parameters of the candidate lattice points on the inner wall of the metal nanopores in the pore-alloy element composite structure after they are occupied by alloy elements, and obtain the relationship between the energy property parameters and the coordination distribution information to predict the energy and structural information of alloy elements at any nanopore inner wall, that is, to obtain the occupied position of alloy elements.

[0013] As a further optimization of the present invention, the metal is a body-centered cubic or face-centered cubic metal.

[0014] As a further optimization of the present invention, step S2 specifically includes the following steps:

[0015] (1) Identify and label the atomic lattice points on the inner wall of the pores in the metal nanoporous stable structure;

[0016] (2) Calculate the coordination number of each atomic lattice point in the inner wall of the hole, and statistically analyze the coordination number distribution of all atomic lattice points in the inner wall of the hole to generate coordination distribution information; wherein, the coordination number is the number of vacant positions adjacent to the atomic lattice point;

[0017] (3) Based on the coordination distribution information, the atomic lattice points on the inner wall of the hole are classified by setting the cutoff radius;

[0018] (4) Select several lattice points from each class as candidate lattice points, replace the candidate lattice points with the alloy element atoms to be calculated, and generate a series of pore-alloy element composite structures.

[0019] As a further optimization of the present invention, in step S3, the energy property parameters include total energy and binding energy.

[0020] The beneficial effects of this invention are as follows:

[0021] Compared with the commonly used high-symmetry point selection method, manual random selection method, and full calculation method, the present invention has the following advantages:

[0022] I. Comparison with the high symmetry point selection method:

[0023] (1) Comprehensive consideration: The high symmetry point selection method only considers points with high symmetry in the metal lattice, which may miss some stable positions with lower energy. However, the present invention comprehensively considers the atomic positions of the entire inner wall of the nanopore through simulated annealing and coordination number analysis, so as not to miss any potential stable positions.

[0024] (2) Better optimization effect: Simulated annealing algorithm can find the global optimum in a larger search space, avoiding the problem of local optima. Therefore, the method of the present invention is more effective in finding the most stable structure of nanopores and the most stable occupancy position of alloying elements.

[0025] II. Comparison with human random selection method:

[0026] (1) Higher efficiency: The manual random selection method may require a lot of calculation to obtain reliable results, which is inefficient. However, the method of the present invention classifies and screens by simulating annealing and accurately calculating coordination number, which greatly reduces the number of candidate structures that need to be calculated and improves the computational efficiency.

[0027] (2) Reliable results: The results of the random selection method may be unstable due to different initial random selections, while the present invention ensures the stability and reliability of the results through systematic simulated annealing and coordination number analysis.

[0028] III. Comparison with all calculation methods:

[0029] (1) Significantly reduced computational load: All computational methods require calculation of all possible structures, resulting in a huge computational load, especially when the nanopores are large, which is difficult to achieve. The method of this invention effectively reduces the number of candidate structures that need to be calculated by simulated annealing and coordination number screening, thus significantly reducing the computational load.

[0030] (2) Higher feasibility: All calculation methods are often difficult to implement in practical applications due to the huge amount of computation, while the method of the present invention can still achieve efficient calculation in large nanopore systems through reasonable screening and optimization steps. Attached Figure Description

[0031] Figure 1 This is a flowchart of the process for finding the most stable nanoporous structure in this invention;

[0032] Figure 2 This is a flowchart of the coordination number analysis and screening process in this invention;

[0033] Figure 3 This is a diagram showing the correlation between the binding energy and coordination number distribution of common alloying elements in nanopores of face-centered cubic aluminum alloys obtained by the method described in this invention. Detailed Implementation

[0034] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0035] This invention proposes a method for predicting the occupancy of alloying elements in metal nanopores, comprising the following steps:

[0036] S1. Construct a simulated annealing model for a stable metal porous structure. By employing simulated annealing, the nanopores with the smallest inner surface area are obtained, representing the most stable nanopore structure. For example... Figure 1 As shown, the specific steps are as follows:

[0037] (1) Initialize the metal supercell and vacancy distribution: Establish the metal supercell and introduce a specific number of vacancy in it to ensure that the given size and vacancy number requirements are met.

[0038] (2) Randomly swap atoms and vacancies: Randomly select a vacancy and a metal atom, and try to swap their positions.

[0039] (3) Calculate the change in surface area: Calculate the Wigner-Seitz surface area of ​​vacancies before and after the metal atom exchanges with the vacancy.

[0040] (4) Decide whether to accept the exchange: If the exchange reduces the surface area, then accept the exchange. If the exchange increases the surface area, then accept the exchange probabilistically according to the Metropolis criterion. The Metropolis criterion is key in the simulated annealing algorithm and is used to determine whether to accept a state with increased energy. Its probability depends on the amount of increase in surface area and the current system temperature.

[0041] (5) Simulated annealing process: The nanoporous structure is continuously optimized through multiple exchange and annealing iterations. During the simulated annealing process, the temperature is gradually reduced, thereby reducing the probability of accepting poor exchanges and eventually converging to a stable structure.

[0042] (6) Obtaining the nanopore with the smallest surface area: The nanopore with the smallest Wegener's surface area is finally obtained, which is the most stable pore structure.

[0043] S2. A comprehensive analysis of the coordination distribution of the inner wall of the nanopores obtained in S1 is performed. Atomic lattice points on the inner wall of the nanopores in the stable structure are identified and marked. The coordination number of each atomic lattice point is calculated to generate coordination distribution information. Based on the coordination distribution information and set classification conditions, the inner wall lattice points are classified. Then, the atoms of the alloying elements to be calculated are replaced on several classified inner wall lattice points to obtain a pore-alloying element composite structure, providing candidate structures for calculating the energy properties of the alloying elements at the nanopores in S3. For example... Figure 2 As shown, the specific steps are as follows:

[0044] (1) Identify and label the atomic lattice points on the inner wall of the porous structure.

[0045] (2) Calculate the coordination number: For each atomic lattice point on the inner wall, output its coordinates and calculate its coordination number, i.e. the number of vacant points adjacent to it.

[0046] (3) Generate coordination distribution information: Statistically analyze the coordination number distribution of all inner wall grid points and generate coordination distribution information.

[0047] (4) Set cutoff radius and classify: Set the cutoff radius (the actual distance between the vacancy grid point and the metal atom, 1 to 10 nearest neighbors are acceptable), filter out the grid point positions of the first nearest neighbors of all vacancy grid points, and classify the inner wall grid points according to the coordination distribution information.

[0048] (5) Select candidate grid points: Select several grid points from each class as candidate grid points.

[0049] (6) Structure generation: Replace the above candidate lattice points with the alloy element atoms to be calculated to generate a series of pore-alloy element composite structures. These structures serve as candidate structures for subsequent calculations.

[0050] S3. Using VASP software, calculate the energy property parameters, including total energy and binding energy, of candidate lattice points on the inner wall of metallic nanopores after they are occupied by alloying elements using density functional theory (DFT). Summarize the correlation between energy property parameters and coordination distribution information to infer the energy and structural information of alloying elements at any nanopore inner wall, and obtain the occupied positions of alloying elements. The specific steps are as follows:

[0051] (1) Using the porous-alloy element composite structure obtained by S2, VASP calculations were performed on each candidate lattice point to obtain the total energy.

[0052] (2) Calculate the binding energy: Based on the total energy obtained from the calculation, calculate the binding energy of the alloying elements.

[0053] (3) Summarize the correlation between energy property parameters and coordination distribution information: Analyze the relationship between total energy and binding energy and coordination number, so as to infer the energy and structural information of alloying elements at the inner wall of any nanopore and obtain the occupied position of alloying elements.

[0054] Figure 3 This is a diagram showing the correlation between the binding energy and coordination number distribution of common alloying elements in nanopores of face-centered cubic aluminum alloys obtained by the method described in this invention.

[0055] The specific correlation is that the higher the coordination number (i.e., the number of first nearest-neighbor vacancies), the greater the overall binding energy and the more stable the structure. In other words, the position with the maximum coordination number is the most stable position occupied by the alloying element. For the common alloying elements Mg, Si, and Zn selected in the figure, when the coordination number reaches its maximum value of 6, the binding energy also reaches its maximum value. The position of the alloying element at this point is the most stable position, with median maximum binding energies of 0.31 eV, 0.35 eV, and 0.88 eV for Mg, Si, and Zn, respectively. Therefore, once the coordination number distribution information of different candidate lattice points is calculated, the lattice point with the largest coordination number is the most stable position occupied by the alloying element, and the corresponding binding energy can be obtained without extensive calculations and screening using VASP software. This principle allows us to determine the most stable position of the alloying element and the corresponding binding energy of the alloying element-pore composite after adding common alloying elements to nanopores of arbitrary size.

[0056] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for predicting the occupancy of alloying elements in metal nanopores, characterized in that, Includes the following steps: Step S1. Obtain a metal nanopore stable structure of arbitrary size based on the metal pore stable structure simulation annealing model, wherein the metal pore stable structure simulation annealing model is constructed based on a simulation method to determine the stable structure and energy of nanopores in metals; Step S2. Identify and label the atomic grid points on the inner wall of the pores in the metal nanopore stable structure, calculate the coordination number of each atomic grid point to generate coordination distribution information, classify the inner wall grid points according to the coordination distribution information and set classification conditions, and replace the alloy element atoms to be calculated on several classified inner wall grid points to obtain the pore-alloy element composite structure. Step S3. Based on density functional theory, calculate the energy property parameters of the candidate lattice points on the inner wall of the metal nanopores in the pore-alloy element composite structure after they are occupied by alloy elements, and obtain the relationship between the energy property parameters and the coordination distribution information to predict the occupied positions of alloy elements in any metal nanopores.

2. The method for predicting the occupancy of alloying elements in metal nanopores according to claim 1, characterized in that, The metal is a body-centered cubic metal or a face-centered cubic metal.

3. The method for predicting the occupancy of alloying elements in metal nanopores according to claim 1, characterized in that, Step S2 specifically includes the following steps: (1) Identify and label the atomic lattice points on the inner wall of the pores in the metal nanoporous stable structure; (2) Calculate the coordination number of each atomic lattice point in the inner wall of the hole, and statistically analyze the coordination number distribution of all atomic lattice points in the inner wall of the hole to generate coordination distribution information; wherein, the coordination number is the number of vacant positions adjacent to the atomic lattice point; (3) Based on the coordination distribution information, the atomic lattice points on the inner wall of the hole are classified by setting the cutoff radius; (4) Select several lattice points from each class as candidate lattice points, replace the candidate lattice points with the alloy element atoms to be calculated, and generate a series of pore-alloy element composite structures.

4. The method for predicting the occupancy of alloying elements in metal nanopores according to claim 1, characterized in that, In step S3, the energy property parameters include total energy and binding energy.

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