Method for predicting occupancy position of alloy element in metal nano hole

Abstract

The invention relates to a method for predicting occupancy positions of alloy elements in metal nano holes, and relates to the technical field of metal material defect prediction.The method comprises the following steps that a stable structure is obtained through simulated annealing, and the most stable structure of the nano holes of different sizes is obtained through a metal hole stable structure simulated annealing model; coordination number analysis and screening: analyzing the coordination distribution condition of the inner wall of the nano hole, and classifying and screening atoms on the inner wall according to the coordination number; and DFT energy calculation and induction: calculating energy property parameters after the candidate lattice points are occupied by the alloy elements by using VASP software based on a DFT method, and inducing an association rule between the energy property parameters and coordination distribution information. According to the method, the defects of a high-symmetry point selection method and an artificial random selection method are overcome, meanwhile, the calculation amount of all calculation methods is greatly reduced, and the method has remarkable advantages in efficiency and reliability.

Description

Technical Field

[0001] The invention relates to the technical field of metal material defect prediction, and in particular to a method for predicting positions occupied by alloy elements in metal nanopores. Background Art

[0002] The formation of nanopores is a common phenomenon in the processing, preparation and service of metal materials. These nanopores often become the segregation points of alloy elements, and then form precipitation phases, affecting the performance of metal materials. Therefore, studying the segregation behavior of alloy elements at nanopores has important scientific and engineering significance for predicting and regulating material properties.

[0003] The core of the study on the segregation behavior of alloy elements in metal nanopores is to calculate the energy of different alloy elements at each lattice point on the inner wall of the nanopore. The main challenge to be solved is how to select the most representative lattice point from a large number of candidate lattice points to reduce the amount of calculation.

[0004] Existing methods have certain limitations in the calculation process. For example, the high symmetry point selection method only considers points with higher symmetry in the metal lattice, which may miss some stable positions with lower energy. The artificial random selection method may require a lot of calculations to get reliable results, which is inefficient. The full calculation method has a huge amount of calculations, especially when the nanopores are large, and it is difficult to implement.

[0005] Announcement No. CN115910244B discloses a simulation method for determining the stable structure and energy of nanopores in aluminum, which is the research result of the inventor. The Wigner-Seitz primitive cell is used to characterize the inner wall structure of the nanopore, and the Wigner-Seitz primitive cell surface area of ​​the aluminum nanopore is calculated by the simulated annealing method. The nanopore with the smallest Wigner-Seitz primitive cell surface area is the most stable structure, and its formation energy is proportional to the Wigner-Seitz primitive cell surface area of ​​the nanopore. Using this method, the stable structure and energy of aluminum nanopores of any size can be obtained very easily.

[0006] Based on the above research results, the present invention provides an efficient grid point screening method to quickly model and screen out the stable positions of alloy elements in nanopores. Summary of the invention

[0007] The purpose of the present invention is to provide a method for accurately and conveniently predicting the positions occupied by alloy elements in metal nanopores, so as to solve the problems mentioned in the background technology.

[0008] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0009] A method for predicting the position occupied by alloy elements in metal nanopores comprises the following steps:

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

[0011] Step S2. Identify and mark the atomic lattice points on the inner wall of the hole in the metal nanopore stable structure, calculate the coordination number of each atomic lattice point to generate coordination distribution information, classify the inner wall lattice points according to the coordination distribution information and set classification conditions, and replace the atoms of the alloy element to be calculated on the classified inner wall lattice points to obtain a hole-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 nanopore in the pore-alloy element composite structure after being occupied by the alloy element, and obtain the relationship between the energy property parameters and the coordination distribution information to predict the energy and structural information of the alloy element at the inner wall of any nanopore, that is, to obtain the occupied position of the alloy element.

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

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

[0015] (1) identifying and marking the atomic lattice points on the inner wall of the pores in the metal nanopore stable structure;

[0016] (2) calculating the coordination number of each atomic lattice point in the inner wall of the hole, and statistically analyzing the distribution of the coordination numbers 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 vacancies adjacent to the atomic lattice point;

[0017] (3) according to the coordination distribution information, setting a cutoff radius to classify the atomic lattice points on the inner wall of the hole;

[0018] (4) Selecting a number of lattice points from each category as candidate lattice points, replacing the candidate lattice points with the alloy element atoms to be calculated, and generating a series of hole-alloy element complex structures.

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

[0020] The beneficial effects of the present invention are:

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

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

[0023] (1) Comprehensive consideration: The high symmetry point selection method only considers points with higher 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 nanopore inner wall through simulated annealing and coordination number analysis, so as not to miss potential stable positions.

[0024] (2) Better optimization effect: The simulated annealing algorithm can find the global optimal solution in a larger search space, avoiding the trouble of the local optimal solution. Therefore, the method of the present invention is more effective in finding the most stable structure of nanopores and the most stable position occupied by alloy elements.

[0025] II. Comparison with the artificial random selection method:

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

[0027] (2) Reliable results: The results of the artificial random selection method may be unstable due to differences in the initial random selection, 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) The amount of calculation is significantly reduced: All calculation methods require calculation of all possible structures, which is very computationally intensive, especially when the nanopores are large, and difficult to implement. The method of the present invention effectively reduces the number of candidate structures that need to be calculated through simulated annealing and coordination number screening, significantly reducing the amount of calculation.

[0030] (2) Higher feasibility: All calculation methods are often difficult to implement in practical applications due to the huge amount of calculation. However, the method of the present invention can still achieve efficient calculation in larger nanopore systems through reasonable screening and optimization steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of the present invention for finding the most stable nanopore structure;

[0032] Figure 2 is a flow chart for obtaining coordination number analysis and screening in the present invention;

[0033] Figure 3 The diagram is a regularity diagram of the association between the binding energy and coordination number distribution information of common alloying elements at nano-pores in a face-centered cubic aluminum alloy obtained by the method of the present invention. DETAILED DESCRIPTION

[0034] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0035] The present invention provides a method for predicting the position occupied by alloy elements in metal nanopores, comprising the following steps:

[0036] S1. Construct a simulated annealing model for the stable structure of metal holes, and use the simulated annealing method to obtain nanoholes with the smallest inner surface, which is the most stable structure of nanoholes. Figure 1 As shown, the specific steps are as follows:

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

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

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

[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 the key to the simulated annealing algorithm and is used to decide whether to accept a state with increased energy. Its probability depends on the amount of surface area increase and the current system temperature.

[0041] (5) Simulated annealing process: The nanopore structure is continuously optimized through multiple exchanges 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 nanopores with the smallest surface area: The nanopores with the smallest Wignerses surface area are finally obtained, which is the most stable pore structure.

[0043] S2, comprehensively analyzing the coordination distribution of the inner wall of the nanopore obtained in S1, identifying and marking the atomic lattice points of the inner wall of the pore in the metal nanopore stable structure, calculating the coordination number of each atomic lattice point to generate coordination distribution information, classifying the inner wall lattice points according to the coordination distribution information and the set classification conditions, and replacing the atoms of the alloy element to be calculated for the classified inner wall lattice points, and obtaining the pore-alloy element complex structure to provide a candidate structure for S3 to calculate the energy properties of the alloy element at the nanopore. Figure 2 As shown, the specific steps are as follows:

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

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

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

[0047] (4) Setting the cutoff radius and classifying: Setting the cutoff radius (the actual distance between the vacant lattice point and the metal atom, 1 to 10 neighbors are acceptable), screening out the lattice point positions of the first neighbors of all vacant lattice points, and classifying the inner wall lattice points according to the coordination distribution information.

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

[0049] (6) Generating structures: Replace the candidate grid points with the alloy element atoms to be calculated to generate a series of hole-alloy element complex structures. These structures serve as candidate structures for subsequent calculations.

[0050] S3. Use VASP software to calculate the energy property parameters of the candidate lattice points on the inner wall of the metal nanopore after being occupied by the alloy elements, including total energy and binding energy, through the density functional theory (DFT) method. Summarize the correlation between the energy property parameters and the coordination distribution information, so as to infer the energy and structural information of the alloy elements at the inner wall of any nanopore and obtain the occupied position of the alloy elements. The specific steps are as follows:

[0051] (1) Using the pore-alloying element complex structure obtained by S2, VASP calculation is performed on each candidate lattice point to obtain the total energy.

[0052] (2) Calculation of binding energy: Based on the calculated total energy, calculate the binding energy of the alloy 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 alloy elements at the inner wall of any nanopore and obtain the occupied position of alloy elements.

[0054] Figure 3 The diagram is a regularity diagram of the association between the binding energy and coordination number distribution information of common alloying elements at nano-pores in a face-centered cubic aluminum alloy obtained by the method of the present invention.

[0055] The specific correlation rule is that when the coordination number (i.e., the number of first-neighbor vacancies) increases, the binding energy is generally larger, and the structure is more stable, that is, the position with the largest coordination number is the most stable position occupied by the alloy element. For the common alloy elements Mg, Si, and Zn selected in the figure, when the coordination number reaches the maximum value of 6, the binding energy also reaches the maximum value. At this time, the position of the alloy element is the most stable position, where the median of the maximum binding energy corresponding to Mg, Si, and Zn is 0.31eV, 0.35eV, and 0.88eV, respectively. Therefore, as long as 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 alloy element, and the corresponding binding energy can be obtained at the same time, without the need for a large number of calculations and screening through the VASP software. Through this rule, the most stable position of the alloy element and the corresponding binding energy size of the alloy element-hole complex can be determined after adding common alloy elements to nanopores of any size.

[0056] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for predicting the position occupied by alloy elements in metal nanopores, characterized in that: The steps include: Step S1. obtaining a metal nanopore stable structure of any size according to a metal pore stable structure simulated annealing model, wherein the metal pore stable structure simulated annealing model is constructed based on a simulation method for determining a nanopore stable structure and energy in a metal; Step S2. Identify and mark the atomic lattice points on the inner wall of the hole in the metal nanopore stable structure, calculate the coordination number of each atomic lattice point to generate coordination distribution information, classify the inner wall lattice points according to the coordination distribution information and set classification conditions, and replace the atoms of the alloy element to be calculated on the classified inner wall lattice points to obtain a hole-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 nanopore in the pore-alloy element composite structure after being occupied by the alloy element, obtain the relationship between the energy property parameters and the coordination distribution information, so as to predict the occupied position of the alloy element in any metal nanopore.

2. A method for predicting the position occupied by alloy elements in metal nanopores according to claim 1, characterized in that: The metals are body-centered cubic metals and face-centered cubic metals.

3. The method for predicting the position occupied by alloy elements in metal nanopores according to claim 1, characterized in that: The step S2 specifically includes the following steps: (1) identifying and marking the atomic lattice points on the inner wall of the pores in the metal nanopore stable structure; (2) calculating the coordination number of each atomic lattice point in the inner wall of the hole, and statistically analyzing the distribution of the coordination numbers 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 vacancies adjacent to the atomic lattice point; (3) according to the coordination distribution information, a cutoff radius is set to classify the atomic lattice points on the inner wall of the hole; (4) Selecting a number of lattice points from each category as candidate lattice points, replacing the candidate lattice points with the alloy element atoms to be calculated, and generating a series of hole-alloy element complex structures.

4. The method for predicting the position occupied by alloy 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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