High-entropy alloy structure file construction method based on atom preferential occupancy characteristics

Abstract

The invention provides a high-entropy alloy structure file construction method based on atom preferential occupancy characteristics. The method specifically comprises the following steps: S1, creating folders in batches, and adding a one-key macro command file * * *. Tcm under the corresponding folders; and S2, extracting occupancy fractions in batches and carrying out graphical representation on the occupancy fractions. And S3, calculating and determining distribution data of various atoms on various sublattices. And S4, adding a selective kinetic marker to the sublattice position. And S5, performing shuffling type random arrangement on atomic positions on various sublattices in the supercell. And S6, distributing atomic position coordinates. And S7, classifying, sorting and combining the same kind of atomic coordinates and atomic numbers, and combining the structural file POSCAR of the high-entropy alloy. According to the method, the high-entropy alloy based on the atom preferential occupancy characteristic behavior is realized. The whole process from data reading to data processing and storage is fully automatic, the efficiency is greatly improved, and errors possibly introduced by manual operation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of metal material calculation and simulation, and in particular to a method for constructing a high entropy alloy structure file based on atomic preferential occupancy characteristics. Background Art

[0002] The new concept of high entropy alloy was first proposed by Taiwanese scholar Ye Junwei in 2004. It adopts a multi-principal component design strategy and tends to form alloys with simple solid solution structures, breaking through the traditional single-principal component or dual-principal component alloy composition design concept. Recent studies have shown that high entropy alloys have demonstrated their excellent performance in many aspects. For example, AlCoCrFeNi shows high strength, Al0.5CoCrCuFeNi has good fatigue resistance, TiZrNbMoVx shows high fracture resistance, and HfNbTiZr has excellent thermal stability. In addition, high entropy alloys such as Co34Cr20Fe34Mn6Ni6 show high ductility, and CoCrFeNi has excellent radiation resistance. These alloys have potential application value in many fields such as metallurgy, catalysts, magnetic materials and nuclear materials. As an emerging material, high entropy alloy (HEA) has attracted much attention due to its unique properties, but its theoretical research is still insufficient. The four well-known effects of high entropy alloys, namely the high entropy effect of thermodynamics, the hysteresis diffusion effect of kinetics, the lattice distortion effect of structure, and the cocktail effect of performance, are often questioned, indicating the insufficiency of the current alloying theory and the research on the relationship between structure and performance of high entropy alloys. At present, the use of the concept of material genetic engineering, especially through high-throughput calculations and experiments, combined with AI material design methods of machine learning, has become an important means to study high entropy alloys. These methods can effectively deal with the challenges brought by the complexity of alloy components, such as the existence of multiple atomic species and their different behaviors in FCC, BCC and HCP crystal structures. In traditional models, such as the random solid solution model (SQS) and the solid solution model (CPA-EMTO), only hypothetical random mixed structures that do not actually exist can be described. These models ignore the differences between atomic species, the differences in alloy phase structures, and the influence of heat treatment temperature to a certain extent. It is necessary and possible to describe the real structure of the preferential occupancy characteristics that actually exist in high entropy alloys in more detail. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a method for constructing a high entropy alloy structure file based on atomic preferential occupancy characteristics, which significantly improves the generation efficiency and reduces the errors that may be introduced by human intervention through a fully automated data reading, processing and storage process, thereby improving the construction efficiency and lowering the technical threshold.

[0004] To achieve the above purpose, the present invention adopts the following technical solution: a method for constructing a high entropy alloy structure file based on the atomic preferential occupancy characteristics, calling the script as follows:

[0005] The name of script 1 is script1-batch create project folders and macro command files ***.tcm;

[0006] According to the end group thermodynamic database, batch create folders according to the number of equimolar components and the initial alphabetical order of element names, and configure the element and composition settings in the one-click macro command file ***.tcm for thermodynamic and phase equilibrium calculations in each folder;

[0007] Script 2 is named script2-AutoHotkey, which automatically calls Thermo-Calc and executes a one-click macro command file ***.tcm for thermodynamic and phase equilibrium calculations;

[0008] Automatically call Thermo-Calc thermodynamics and phase equilibrium software in Windows system, execute the macro command file ***.tcm added by script 1, and automatically perform thermodynamic calculations and generate phase equilibrium data, including thermodynamic data and occupancy fraction data that vary with temperature;

[0009] The name of script 3 is script3-batch sorting of placeholder information and drawing images;

[0010] According to the occupancy fraction file YSITE-T.EXP in various multi-principal element folders obtained by script 2, the occupancy fraction that changes with temperature within the calculated set temperature range is extracted, and formatted data processing and graphical representation are performed;

[0011] The name of script 4 is script4-based on the batch construction of the structure file POSCAR_Si and the high entropy alloy structure file POSCAR of the atomic distribution of the 1a and 3c sublattices of the high entropy alloy;

[0012] According to the occupancy fraction after formatting data obtained in script 3 and the expansion cell size set according to the computing resources, the structure file POSCAR_Si of the 1a and 3c sublattice atomic distribution based on the atomic preferential occupancy characteristics is constructed, and the formatted structure file POSCAR of the synthesized high entropy alloy is further nested;

[0013] The following steps are involved:

[0014] Step S1, batch create folders and add a one-key macro command file ***.tcm in the corresponding folder; call script 1, based on the formatted Gibbsenergy thermodynamic database file of the N-principal high entropy alloy end group compound that has been established and copied into the folder

[0015] HEAs_EndMember_G-T.TDB, according to the element combination from equimolar 3 principal elements to equimolar N principal elements, and the initial alphabetical order of the element names, batch create folders and add a one-click macro command file ***.tcm to the corresponding folders; further call script 2, automatically call the Thermo-Calc thermodynamic and phase equilibrium calculation software package in Windows, and execute the one-click macro command file ***.tcm to automatically generate the corresponding thermodynamic data and the YSITE-T.EXP file containing the occupancy fraction;

[0016] Step S2, batch occupancy fraction and graphical representation; use script 3 to batch extract the occupancy fraction data of each system, and remove the repeated values ​​involved in the temperature segment output during Thermo-Calc calculation, arrange them from small to large according to temperature T, and generate YSITE-T_output.xlsx in the corresponding folder, and draw the corresponding YSITE-T.png image according to the data in YSITE-T_output.xlsx;

[0017] Step S3, calculate and determine the distribution data of various atoms on various sublattices; select a certain equilibrium heat treatment temperature of interest, extract the corresponding occupancy fraction, and establish an acceptable total number of atoms after cell expansion based on the expected application of the structure file and the alloy prototype structure model; Step S3 is based on the Python programming language to achieve the establishment of the number of various atoms of the corresponding system on various sublattices;

[0018] Step S4, adding selective dynamics marks to the sublattice positions; according to the determined supercell size scheme, obtaining the position coordinate files 1a.vasp and 3c.vasp of the 1a and 3c sublattices, and further filling the TTT mark at the end of the atomic coordinates after the sublattice position files, and modifying the origin TTT with coordinates 000 to FFF; in addition, selecting an atom on the x-axis, y-axis and z-axis respectively, and marking the TTT behind the coordinates as FTT, TFT and TTF accordingly. This is for selective dynamics manipulation when the first-principles calculation method is used to relax the crystal structure in the subsequent process;

[0019] Step S5, shuffle randomly arrange the atomic positions on various sublattices in the supercell to generate a sublattice position random arrangement template file; use Python's shuffle_and_save() function to randomly distribute and save the file content according to the determined supercell size plan to ensure the randomness of the data and the repeatability of the processing; that is, shuffle randomly distribute the coordinates of all atomic positions on various sublattices to generate a sublattice position random arrangement template file POSCAR_Si;

[0020] Step S6, allocating atomic position coordinates; according to the results of step S3, step S4 and step S5, allocating the positions of various types of atoms on various types of sublattices, and obtaining the coordinate allocation structure file POSCAR_Si_HEA of different atoms on various types of sublattices;

[0021] Step S7 classifies and merges similar atomic coordinates and atomic numbers to form a high entropy alloy structure file POSCAR; based on the coordinates of different atoms on various sublattices, the structure file POSCAR_Si is allocated, and the similar atomic coordinates and atomic numbers are classified and merged in alphabetical order of element symbols to form a high entropy alloy structure file POSCAR; wherein the initial basis vector adopts the supercell initial basis vector after supercell generation of the prototype structure file.

[0022] In a preferred embodiment, in step S1, the automation script 2 includes the following internal operation steps:

[0023] Step S11, initialization settings; use #NoEnv to disable the default environment settings and optimize script performance; set SendMode Input to improve the speed and reliability of command transmission; remove the line processing speed limit through SetBatchLines,-1 to improve execution efficiency; locate the script working directory to the location of the script to ensure that the path is correctly parsed;

[0024] Step S12, path definition; RootPath is defined as the main directory of the data file; SoftwarePath is defined as the execution path of the thermodynamic calculation software;

[0025] Step S13, file retrieval and execution; traverse all subdirectories under RootPath to find the .TCM file; if the file exists, use the Run command with the path to start the computing software;

[0026] Step S14, automated operation; use WinWait and WinWaitActive to ensure that the software is ready; send keyboard commands such as Send,{F4} to start the calculation, and set the necessary delay Sleep,1500 to adapt to the software response;

[0027] Step S15, loop processing; after the operation is completed, the script automatically returns to the beginning of the loop and continues to the next folder until all processing is completed.

[0028] In a preferred embodiment, in step S2, script 3 batch extracts the occupancy fractions within a certain temperature range and graphically represents the occupancy fractions, including the following steps:

[0029] Step S21, initialization settings and file processing; define function process_exp_file, parse the specified YSITE-T.EXP placeholder file, use regular expressions to match the specified pattern to determine the data column name and extract relevant data, use a dictionary to collect data, the dictionary key is the chemical component, the value is a list containing temperature and placeholder values, convert the collected data into multiple pandas DataFrames, each DataFrame represents the data of a chemical component, use pd.concat to merge all DataFrames to form a comprehensive DataFrame, and return after resetting the index;

[0030] Step S22, traverse the directory and process the files; define the function process_exp_and_save_excel, check whether each file matches 'YSITE-T.EXP'; and confirm that the file size exceeds 10KB; call the save_exp_as_excel function for the qualified files to process and save the data;

[0031] Step S23, save the data as Excel; define the save_exp_as_excel function, call the process_exp_file function to obtain the processed data, further filter and sort the data, and use the to_excel method of pandas to save the processed data as an Excel file;

[0032] Step S24, traverse the directory and draw the graph: define the function plot_data_from_excel, use the read_excel method of pandas to convert the Excel file into a DataFrame, use matplotlib to draw the graph and save it as a PNG file.

[0033] In a preferred embodiment, the step S3 of calculating and determining the distribution data of various atoms on various sublattices comprises the following steps:

[0034] Step S31, initialization and data reading: using the read_excel method of the pandas library to read the Excel file of a specific temperature from the specified path into a DataFrame;

[0035] Step S32, the distribution data of various atoms on various sublattices are determined; the function adjust_values_to_target is defined, and the number of atoms that should exist on various sublattices is determined according to the determined expansion cell size, and then the cell is expanded and the number of atoms on various sublattices is preliminarily obtained by rounding off, and then it is judged whether the number of atoms on various sublattices at this time is consistent with the number of atoms that should exist on various sublattices. If the number of atoms on the sublattice is less than the number of atoms that should exist at this time, the atom with the smallest value before rounding off and after rounding off in the sublattice is selected, and it is decremented by 1; similarly, if the number of atoms on the sublattice is more than the number of atoms that should exist at this time, the atom with the largest value before rounding off and after rounding off in the sublattice is selected, and it is incremented by 1; then it is iterated to determine whether the number of atoms on various sublattices is correct until the atoms on all sublattices meet the requirements;

[0036] Step S33, writing to Excel table: use the to_excel method of pandas to save the processed data as an Excel file.

[0037] In a preferred embodiment, in step S4, the atomic positions on various sublattices in the supercell are randomly arranged in a shuffled manner to generate a template file for random arrangement of sublattice positions; step S4 includes two modes: manual operation and automatic operation by a python programming language program; wherein, in manual operation, the position coordinates on different sublattices are first separated, and then copied into Excel, and random numbers are attached to the right, and then custom sorting is performed, with the column number of the random number as the main keyword, the sorting basis is the value, and the order is ascending, so that the random numbers are arranged in ascending order, so that the regular three-dimensional coordinates are correspondingly converted into randomly distributed coordinates, and then the random numbers are sorted in order. The elements on the sublattice are filled into the corresponding coordinates to obtain the position coordinates of each atom on the sublattice; the above operation is repeated for the position coordinates on various sublattices to obtain the position coordinates of each atom on various sublattices; based on the python programming language, the above steps are fully automated; first, the position coordinates on different sublattices are separated and saved in the ordered position files DataFrame_Si of different sublattice supercells, where i represents different types of sublattice types, such as high entropy alloys of FCC_L12 structure, i represents 1a or 3c; for high entropy alloys of BCC_B2 structure, i represents 1a or 1b; and for HCP_D0 19 The high entropy alloy of the structure, i represents 2c or 6h; then use the for loop, set the random number seed 42, shuffle the atomic position coordinates on various sublattices and randomly distribute them; then fill in the element symbol and the number of atoms on the sublattice in turn before the position coordinate list, and you can get the position coordinate allocation structure file POSCAR_Si corresponding to the different types of constituent atoms on the sublattice.

[0038] In a preferred embodiment, in step S4, TTT marks are filled in at the end of the coordinates of various sublattices in the supercell, and the origin TTT with coordinate 000 is modified to FFF; in addition, an atom is selected on the x-axis, y-axis and z-axis respectively, and the TTT after the coordinate is marked as FTT, TFT and TTF accordingly. In order to perform selective dynamics manipulation when the crystal structure is relaxed by the first-principles calculation method later, the atomic positions on various sublattices in the supercell are randomly shuffled in step S5 to generate a template file for random arrangement of sublattice positions.

[0039] In a preferred embodiment, step S7 classifies and merges similar atomic coordinates and atomic numbers; based on the coordinate allocation structure file POSCAR_Si of different atoms on various sublattices, the similar atomic coordinates and atomic numbers are classified and merged in alphabetical order of element symbols to form the structure file POSCAR of high entropy alloy; wherein the initial basis vector adopts the supercell initial basis vector after supercell generation of the prototype structure file.

[0040] Compared with the prior art, the present invention has the following beneficial effects: The purpose of the present invention is to provide a method for constructing a high entropy alloy structure file based on the atomic preferential occupancy characteristics. Abandoning the ideal mixed model that is traditionally adopted by most people but is unreasonable (that is, the assumption that different types of atoms occupy positions randomly on different types of sublattices is unreasonable), the preferential occupancy behavior of atoms is reasonably and accurately predicted, and the batch automatic generation of POSCAR files of multi-principal alloys is realized. Through the fully automated data reading, processing and saving process, the generation efficiency is significantly improved, and the errors that may be introduced by manual intervention are reduced, the construction efficiency is improved, and the technical threshold is lowered. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of a preferred embodiment of the present invention;

[0042] Figure 2 A formatted database fragment display for a preferred embodiment of the present invention;

[0043] Figure 3 Screenshot of the thermodynamics, site occupancy fraction and structure file folder for a representative equimolar high entropy alloy system:

[0044] Figure 4 A graph showing the relationship between the site occupancy fraction and temperature of the FCC_CrFeNiV high entropy alloy of a preferred embodiment of the present invention;

[0045] Figure 5A schematic diagram of a model prototype FCC_L12_AuCu3 of an FCC structure according to a preferred embodiment of the present invention;

[0046] Figure 6 Schematic diagram of 3×3×3 expansion cell of FCC_L12_AuCu3, a model prototype of the FCC structure of the preferred embodiment of the present invention;

[0047] Figure 7 The FCC_3x3x3-1a sublattice of the preferred embodiment of the present invention;

[0048] Figure 8 The FCC_3x3x3-3c sublattice of the preferred embodiment of the present invention;

[0049] Fig. 9 The randomly distributed FCC_3x3x3-1a sublattice of the preferred embodiment of the present invention;

[0050] Fig.10 The randomly distributed FCC_3x3x3-3c sublattice of the preferred embodiment of the present invention;

[0051] Fig.11 An example of POSCAR formatted in FCC_CrFeNiV high entropy alloy according to a preferred embodiment of the present invention.

[0052] Fig.12 A schematic diagram of POSCAR constructed by nesting sublattice combinations of FCC_CrFeNiV high entropy alloys according to a preferred embodiment of the present invention; DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0054] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0055] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0056] A method for constructing high entropy alloy structure files based on atomic preferential occupancy characteristics, reference Figure 1-12 , including the following steps:

[0057] Step S1, batch create folders and add a one-click macro command file ***.tcm to the corresponding folders. Call script 1, based on the already established formatted Gibbs energy thermodynamic database file HEAs_EndMember_G-T.TDB of the N-principal high entropy alloy end group compound, batch create folders and add a one-click macro command file ***.tcm to the corresponding folders according to the element combination from equimolar 3 principal elements to equimolar N principal elements. Further call script 2, automatically call the Thermo-Calc thermodynamic and phase equilibrium calculation software package in Win, and based on the one-click macro command file ***.tcm, automatically generate the corresponding thermodynamic data, as well as the YSITE-T.EXP file containing the occupancy fraction.

[0058] Step S2, batch extract the occupancy fractions within a certain temperature range and represent them in images. Use script 3 to batch extract the occupancy fraction data of each system, remove the repeated values ​​involved in the temperature segment output during Thermo-Calc calculation, arrange them from small to large by temperature (T), and generate YSITE-T_output.xlsx in the corresponding folder, and draw the corresponding YSITE-T.png image based on the data in YSITE-T_output.xlsx.

[0059] Step S3, calculate and determine the distribution data of various atoms on various sublattices. Select a certain equilibrium heat treatment temperature of interest, extract the corresponding occupancy fraction, and establish an acceptable total number of atoms after cell expansion based on the expected application of the structure file and the alloy prototype structure model (such as for statistical analysis of atomic distribution characteristics, select a supercell size containing tens of thousands of atoms in total, and further use it in first-principles calculations, select a supercell size containing hundreds of atoms in total. Correspondingly, the total number of atoms will be closely related to the supercell dimension). This step is based on the python programming language to achieve the establishment of the number of various atoms of the corresponding system on various sublattices.

[0060] Step S4, add selective dynamics marks to the sublattice positions. According to the determined supercell size scheme, obtain the position coordinate files 1a.vasp and 3c.vasp of the 1a and 3c sublattices, and further fill in the TTT mark at the end of the atomic coordinates after the sublattice position files, and modify the origin TTT with coordinates 000 to FFF; in addition, select an atom on the x-axis, y-axis and z-axis respectively, and mark the TTT behind the coordinates as FTT, TFT and TTF accordingly. This is for the subsequent use of the first-principles calculation method to relax the crystal structure and perform selective dynamics manipulation.

[0061] Step S5, shuffle the atomic positions on various sublattices in the supercell to generate a sublattice position random arrangement template file. According to the determined supercell size scheme, use Python's shuffle_and_save() function to randomly distribute and save the file content to ensure the randomness of the data and the repeatability of the processing. That is, shuffle the coordinates of all atomic positions on various sublattices to generate a sublattice position random arrangement template file POSCAR_Si.

[0062] Step S6, assigning atomic position coordinates. According to the results of steps S3, S4 and S5, the positions of various types of atoms on various types of sublattices are assigned to obtain the coordinate assignment structure file POSCAR_Si_HEA of different atoms on various types of sublattices.

[0063] Step S7 classifies and merges the coordinates and atomic numbers of similar atoms and forms a structure file POSCAR of a high entropy alloy. Based on the coordinates of different atoms on various sublattices, the structure file POSCAR_Si is allocated, and the coordinates and atomic numbers of similar atoms are classified and merged in alphabetical order of element symbols to form a structure file POSCAR of a high entropy alloy. Among them, the initial basis vector adopts the initial basis vector of the supercell after the prototype structure file is generated by supercell.

[0064] In step S1, script 1 is used to call the established formatted Gibbs energy thermodynamic database file HEAs_EndMember_G-T.TDB of the N-principal element high entropy alloy end group compound, and the database can further expand the element types, end group types and corresponding Gibbs energy thermodynamic data.

[0065] The formatted Gibbs energy thermodynamic database file of high entropy alloy end-group compounds is described in detail in the authorized patent ZL 2021100207115 "Calculation method of high entropy alloy configuration entropy based on atomic site ordering behavior" obtained by the inventor's research group in the early stage. It records the end-group compounds of high entropy alloys based on the sublattice model, the formatted Gibbs energy thermodynamic function expressions and database files at different temperatures, and the database can further expand the element types, end-group types and corresponding Gibbs energy thermodynamic data. This patent fully utilizes the end-group thermodynamic database, batches, automatically calculates and constructs the structure file of equimolar high entropy alloy components, and lays a structural foundation for further predicting the performance of high entropy alloys.

[0066] In step S1, the automation script 2 includes the following internal operation steps: Step S11, initialization settings. Use #NoEnv to disable the default environment settings and optimize script performance. Set SendMode Input to improve the speed and reliability of command transmission. Use SetBatchLines,-1 to remove the line processing speed limit to improve execution efficiency. Locate the script working directory to the location of the script to ensure that the path is correctly resolved;

[0067] Step S12, path definition. RootPath is defined as the main directory of the data file. SoftwarePath is defined as the execution path of the thermodynamic calculation software;

[0068] Step S13, file retrieval and execution. Traverse all subdirectories under RootPath to find the .TCM file. If the file exists, use the Run command with the path to start the computing software;

[0069] Step S14, automated operation. Use WinWait and WinWaitActive to ensure that the software is ready. Send keyboard commands such as Send,{F4} to start the calculation, and set the necessary delay Sleep,1500 to adapt to the software response;

[0070] Step S15, loop processing. After the operation is completed, the script automatically returns to the beginning of the loop and continues to the next folder until all processing is completed.

[0071] In step S2, script 3 batch extracts the occupancy fractions within a certain temperature range and graphically represents the occupancy fractions, including the following steps:

[0072] Step S21, initialization settings and file processing. Define the function process_exp_file, parse the specified YSITE-T.EXP placeholder file, use regular expressions to match the specified pattern to determine the data column name and extract relevant data, use a dictionary to collect data, the dictionary key is the chemical component, the value is a list containing temperature and placeholder values, convert the collected data into multiple pandas DataFrames, each DataFrame represents the data of a chemical component, use pd.concat to merge all DataFrames to form a comprehensive DataFrame, and return after resetting the index;

[0073] Step S22, traverse the directory and process the files. Define the function process_exp_and_save_excel, check whether each file matches 'YSITE-T.EXP' (not case sensitive), and confirm that the file size exceeds 10KB. For the qualified files, call the save_exp_as_excel function to process and save the data;

[0074] Step S23, save the data as Excel. Define the function save_exp_as_excel, call the process_exp_file function to obtain the processed data, further filter and sort the data, and use the to_excel method of pandas to save the processed data as an Excel file;

[0075] Step S24, traverse the directory and draw the graph: define the function plot_data_from_excel, use the read_excel method of pandas to convert the Excel file into a DataFrame, use matplotlib to draw the graph and save it as a PNG file.

[0076] Step S3 calculates and determines the distribution data of various atoms on various sublattices, including the following steps:

[0077] Step S31, initialization and data reading. Use the read_excel method of the pandas library to read the Excel file of a specific temperature from the specified path into a DataFrame;

[0078] Step S32, the distribution data of various atoms on various sublattices are determined. Define the function adjust_values_to_target, and according to the determined cell expansion size, the number of atoms that should exist on each sublattice can be determined, and then the cell is expanded and the rounding method is used to preliminarily obtain the number of atoms on each sublattice, and then respectively determine whether the number of atoms on each sublattice at this time is consistent with the number of atoms that should exist on each sublattice. If the number of atoms on the sublattice is less than the number of atoms that should exist at this time, the atom with the smallest value before rounding and after rounding in the sublattice is selected and it is decremented by 1. Similarly, if the number of atoms on the sublattice is more than the number of atoms that should exist at this time, the atom with the largest value before rounding and after rounding in the sublattice is selected and it is incremented by 1. Then continue to iterate to determine whether the number of atoms on each sublattice is correct until the atoms on all sublattices meet the requirements;

[0079] Step S33, writing to Excel table: use the to_excel method of pandas to save the processed data as an Excel file.

[0080] In step S4, the atomic positions on various sublattices in the supercell are randomly arranged in a shuffled manner, and a template file of random arrangement of sublattice positions is generated. Step S4 includes two modes of manual operation and automatic operation of the python programming language program. Wherein, during manual operation, the position coordinates on different sublattices are first separated, then copied to Excel, and random numbers are attached to the right, and then custom sorting is performed, with the column number of the random number as the main keyword, the sorting basis is the value, and the order is ascending, so that the random numbers are arranged in ascending order, so that the regular three-dimensional coordinates are correspondingly changed into randomly distributed coordinates, and then the elements on the sublattice are filled into the corresponding coordinates in order to obtain the position coordinates of each atom on the sublattice. The above operation is repeated for the position coordinates on various sublattices, and the position coordinates of each atom on various sublattices are obtained, and finally the position coordinates of all atoms on the corresponding sublattice are randomly distributed in a shuffled manner. To realize automatic process flow, the present invention is based on the python programming language to realize the complete automation of the above steps. First, the position coordinates on different sublattices are separated and saved in the ordered position files DataFrame_Si of different sublattice supercells (i represents different types of sublattice types, such as FCC_L12 high entropy alloys, i represents 1a or 3c; for BCC_B2 high entropy alloys, i represents 1a or 1b; and for HCP_D0 19 A high entropy alloy with a structure, i represents 2c or 6h); then use a for loop to set the random number seed 42, shuffle the atomic position coordinates on various sublattices and randomly distribute them, and then fill in the element symbol and the number of atoms on the sublattice in turn before the position coordinate list, and you can get the position coordinate allocation structure file POSCAR_Si corresponding to the different types of constituent atoms on the sublattice.

[0081] In step S4, the TTT mark is filled in at the end of the coordinates of various sublattices in the supercell, and the origin TTT with coordinate 000 is modified to FFF; in addition, an atom is selected on the x-axis, y-axis and z-axis respectively, and the TTT after the coordinate is marked as FTT, TFT and TTF accordingly. In order to perform selective dynamics manipulation when the crystal structure is relaxed by the first-principles calculation method later, the atomic positions on various sublattices in the supercell are randomly shuffled in step S5 to generate a template file for random arrangement of sublattice positions.

[0082] Step S7 classifies and merges the coordinates and atomic numbers of similar atoms. Based on the coordinates of different atoms on various sublattices, the structure file POSCAR_Si is allocated, and the coordinates and atomic numbers of similar atoms are classified and merged in alphabetical order of element symbols to form the structure file POSCAR of the high entropy alloy. Among them, the initial basis vector adopts the supercell initial basis vector after the prototype structure file is generated by supercell.

[0083] In this example, we take the 13-element end-group thermodynamic database constructed by our research group to batch construct the equimolar FCC single-phase alloy POSCAR as an example to provide a method for constructing a high-entropy alloy structure file based on the atomic preferential occupancy characteristics. Figures 1 to 12 , including the following steps:

[0084] Step S1, select an equimolar alloy system of FCC single-phase structure, based on the established formatted thermodynamic database of terminal compounds such as Figure 2 As shown, use the automation script to automatically call Thermo-Calc in Windows, and use the one-click macro command file ***.tcm to automatically generate the corresponding thermodynamic data and the .EXP file containing the occupancy fraction as shown in Figure 3 As shown, the single-phase FCC prototype is L12_AuCu3, which contains two sets of sublattices, 1a and 3c. Figure 5 , 6 shown.

[0085] Step S2, batch extract the occupancy data of each system and remove the duplicate values ​​and arrange them in ascending order according to the temperature T, and generate SITE-T_output.xlsx in the corresponding folder, and draw the corresponding YSITE-T.png image according to the data in SITE-T_output.xlsx, for example, the YSITE-T.png of the FCC_CrFeNiV high entropy alloy system is as follows Figure 4 shown.

[0086] Step S3, extract the occupancy fraction of each system at a temperature of 973K, select a 3×3×3 expansion scheme, and then normalize the occupancy fraction based on the Python programming language to determine the number of various atoms on various sublattices of the corresponding system under the expansion scheme. For example, the occupancy fraction of the FCC_CrFeNiV high entropy alloy with an equimolar atomic ratio of the FCC structure when the high entropy alloy reaches equilibrium after heat treatment at 973K is (Cr 0.0027 Fe 0.0181 Ni 0.9792 V 0.0000 ) 1a (Cr 0.3324 Fe 0.3273 Ni 0.0069 V 0.3333 )3c , after normalization, it is (Ni 27 ) 1a +(Cr 27 Fe 27 V 27 ) 3c =(Ni 27 ) 1a (Cr 27 Fe 27 V 27 ) 3c ;

[0087] Step S4, according to the determined 3×3×3 cell expansion scheme, the position coordinates of the sublattice lattices 1a and 3c are determined respectively as follows: Figure 7 , 8 As shown, all atomic position coordinates on the corresponding sublattice are then randomly distributed in a shuffled manner.

[0088] Step S5, the number of atoms of each type on the 1a and 3c sublattices of the corresponding system under the determined 3×3×3 expansion scheme and the sublattice lattice position coordinates after shuffled random distribution are matched in sequence to obtain the coordinates of each atom that satisfies the site ordering of the FCC_CrFeNiV high entropy alloy, such as Fig. 9 , 10 shown.

[0089] Step S6, extract the lattice vector and necessary parameters of the 3×3×3 expanded cell with L12_AuCu3 as the prototype, and then write the corresponding atomic coordinates on each sublattice in the order of elements, extract the occupancy information of the high entropy alloy according to the temperature of 973K, and obtain the POSCAR file based on the atomic preferential occupancy characteristics, as well as the POSCAR file of the atomic distribution on the 1a and 3c sublattices. For example, the sublattice nesting modeling process of FCC_CrFeNiV high entropy alloy is as follows Fig.12 shown.

Claims

1. A method for constructing a high entropy alloy structure file based on atomic preferential occupancy characteristics, characterized in that: Call the script as follows: The name of script 1 is script1-batch create project folders and macro command files ***.tcm; According to the end group thermodynamic database, batch create folders according to the number of equimolar components and the initial alphabetical order of element names, and configure the element and composition settings in the one-click macro command file ***.tcm for thermodynamic and phase equilibrium calculations in each folder; Script 2 is named script2-AutoHotkey, which automatically calls Thermo-Calc and executes a one-click macro command file ***.tcm for thermodynamic and phase equilibrium calculations; Automatically call Thermo-Calc thermodynamics and phase equilibrium software in Windows system, execute the macro command file ***.tcm added by script 1, and automatically perform thermodynamic calculations and generate phase equilibrium data, including thermodynamic data and occupancy fraction data that vary with temperature; The name of script 3 is script3-batch sorting of placeholder information and drawing images; According to the occupancy fraction file YSITE-T.EXP in various multi-principal element folders obtained by script 2, the occupancy fraction that changes with temperature within the calculated set temperature range is extracted, and formatted data processing and graphical representation are performed; The name of script 4 is script4-based on the batch construction of the structure file POSCAR_Si and the high entropy alloy structure file POSCAR of the atomic distribution of the 1a and 3c sublattices of the high entropy alloy; According to the occupancy fraction after formatting data obtained in script 3 and the expansion cell size set according to the computing resources, the structure file POSCAR_Si of the 1a and 3c sublattice atomic distribution based on the atomic preferential occupancy characteristics is constructed, and the formatted structure file POSCAR of the synthesized high entropy alloy is further nested; The following steps are involved: Step S1, batch create folders and add a one-click macro command file ***.tcm to the corresponding folders; call script 1, based on the formatted Gibbsenergy thermodynamic database file HEAs_EndMember_G-T.TDB of the N-principal high entropy alloy end group compound that has been established and copied into the folder, batch create folders according to the element combination from equimolar 3 principal elements to equimolar N principal elements, and the initial alphabetical order of the element names, and add a one-click macro command file ***.tcm to the corresponding folder; further call script 2, automatically call the Thermo-Calc thermodynamic and phase equilibrium calculation software package in Windows, and execute the one-click macro command file ***.tcm, automatically generate corresponding thermodynamic data, and a YSITE-T.EXP file containing the occupancy fraction; Step S2, batch extract the occupancy fraction and represent it in a graphical manner; use script 3 to batch extract the occupancy fraction data of each system within the set temperature range, and remove the repeated values ​​involved in the temperature segment output during Thermo-Calc calculation, and arrange them from small to large according to the temperature T, and generate YSITE-T_output.xlsx in the corresponding folder, and draw the corresponding YSITE-T.png image according to the data in YSITE-T_output.xlsx; Step S3, calculate and determine the distribution data of various atoms on various sublattices; select a certain equilibrium heat treatment temperature of interest, extract the corresponding occupancy fraction, and establish an acceptable total number of atoms after cell expansion based on the application expectations of the structure file and the alloy prototype structure model and the current computing resource conditions; Step S3 is based on the Python programming language to achieve the establishment of the number of various atoms of the corresponding system on various sublattices; Step S4, adding selective dynamics marks to the sublattice positions; according to the determined supercell size scheme, obtain the position coordinate files 1a.vasp and 3c.vasp of the 1a and 3c sublattices, and further fill in the TTT mark at the end of the atomic coordinates after the sublattice position files, and modify the origin TTT with coordinates 000 to FFF; in addition, select an atom on the x-axis, y-axis and z-axis respectively, and mark the TTT behind the coordinates as FTT, TFT and TTF accordingly. This is for the selective dynamics manipulation when the first-principles calculation method is used to relax the crystal structure in the subsequent process; Step S5, shuffle randomly arrange the atomic positions on various sublattices in the supercell to generate a sublattice position random arrangement template file; use Python's shuffle_and_save() function to randomly distribute and save the file content according to the determined supercell size plan to ensure the randomness of the data and the repeatability of the processing; that is, shuffle randomly distribute the coordinates of all atomic positions on various sublattices to generate a sublattice position random arrangement template file POSCAR_Si; Step S6, allocating atomic position coordinates; according to the results of step S3, step S4 and step S5, allocating the positions of various types of atoms on various types of sublattices, and obtaining the coordinate allocation structure file POSCAR_Si_HEA of different atoms on various types of sublattices; Step S7 classifies and merges similar atomic coordinates and atomic numbers to form a high entropy alloy structure file POSCAR; based on the coordinates of different atoms on various sublattices, the structure file POSCAR_Si is allocated, and the similar atomic coordinates and atomic numbers are classified and merged in alphabetical order of element symbols to form a high entropy alloy structure file POSCAR; wherein the initial basis vector adopts the supercell initial basis vector after supercell generation of the prototype structure file.

2. A method for constructing a high entropy alloy structure file based on atomic preferential occupancy characteristics according to claim 1, characterized in that: In step S1, the automation script 2 includes the following internal operation steps: Step S11, initialization settings; use #NoEnv to disable the default environment settings and optimize script performance; set SendModeInput to improve the speed and reliability of command transmission; remove the line processing speed limit through SetBatchLines,-1 to improve execution efficiency; locate the script working directory to the location of the script to ensure that the path is correctly parsed; Step S12, path definition; RootPath is defined as the main directory of the data file; SoftwarePath is defined as the execution path of the thermodynamic calculation software; Step S13, file retrieval and execution; traverse all subdirectories under RootPath to find the .TCM file; if the file exists, use the Run command with the path to start the computing software; Step S14, automated operation; use WinWait and WinWaitActive to ensure that the software is ready; send keyboard commands such as Send,{F4} to start the calculation, and set the necessary delay Sleep,1500 to adapt to the software response; Step S15, loop processing; after the operation is completed, the script automatically returns to the beginning of the loop and continues to the next folder until all processing is completed.

3. A method for constructing a high entropy alloy structure file based on atomic preferential occupancy characteristics according to claim 1, characterized in that: In step S2, script 3 batch extracts the occupancy fraction and represents it in images, including the following steps: Step S21, initialization settings and file processing; define function process_exp_file, parse the specified YSITE-T.EXP placeholder file, use regular expressions to match the specified pattern to determine the data column name and extract relevant data, use a dictionary to collect data, the dictionary key is the chemical component, the value is a list containing temperature and placeholder values, convert the collected data into multiple pandas DataFrames, each DataFrame represents the data of a chemical component, use pd.concat to merge all DataFrames to form a comprehensive DataFrame, and return after resetting the index; Step S22, traverse the directory and process the files; define the function process_exp_and_save_excel, check whether each file matches 'YSITE-T.EXP'; and confirm that the file size exceeds 10KB; call the save_exp_as_excel function for the qualified files to process and save the data; Step S23, save the data as Excel; define the save_exp_as_excel function, call the process_exp_file function to obtain the processed data, further filter and sort the data, and use the to_excel method of pandas to save the processed data as an Excel file; Step S24, traverse the directory and draw the graph: define the function plot_data_from_excel, use the read_excel method of pandas to convert the Excel file into a DataFrame, use matplotlib to draw the graph and save it as a PNG file.

4. A method for constructing a high entropy alloy structure file based on atomic preferential occupancy characteristics according to claim 1, characterized in that: Step S3 calculates and determines the distribution data of various atoms on various sublattices, including the following steps: Step S31, initialization and data reading: using the read_excel method of the pandas library to read the Excel file of a specific temperature from the specified path into the DataFrame; Step S32, the distribution data of various atoms on various sublattices are determined; the function adjust_values_to_target is defined, and the number of atoms that should exist on various sublattices is determined according to the determined expansion cell size, and then the cell is expanded and the number of atoms on various sublattices is preliminarily obtained by rounding off, and then it is judged whether the number of atoms on various sublattices at this time is consistent with the number of atoms that should exist on various sublattices. If the number of atoms on the sublattice is less than the number of atoms that should exist at this time, the atom with the smallest value before rounding off and after rounding off in the sublattice is selected, and it is decremented by 1; similarly, if the number of atoms on the sublattice is more than the number of atoms that should exist at this time, the atom with the largest value before rounding off and after rounding off in the sublattice is selected, and it is incremented by 1; then it is iterated to determine whether the number of atoms on various sublattices is correct until the atoms on all sublattices meet the requirements; Step S33, writing to Excel table: use the to_excel method of pandas to save the processed data as an Excel file.

5. The method for constructing a high entropy alloy structure file based on atomic preferential occupancy characteristics according to claim 1, characterized in that: In step S4, the atomic positions on various sublattices in the supercell are randomly arranged in a shuffled manner to generate a template file for random arrangement of sublattice positions; step S4 includes two modes: manual operation and automatic operation by a python programming language program; wherein, in manual operation, the position coordinates on different sublattices are first separated, and then copied into Excel, and random numbers are attached to the right, and then custom sorting is performed, with the column number of the random number as the main keyword, the sorting basis is the value, and the order is ascending, so that the random numbers are arranged in ascending order, so that the regular three-dimensional coordinates are correspondingly converted into randomly distributed coordinates, and then the elements on the sublattices are arranged in order. Fill the elements to the corresponding coordinates to obtain the position coordinates of each atom on the sublattice; repeat the above operation for the position coordinates on various sublattices to obtain the position coordinates of each atom on various sublattices; based on the python programming language, the above steps are fully automated; first, the position coordinates on different sublattices are separated and saved in the ordered position files DataFrame_Si of different sublattice supercells, where i represents different types of sublattice types, such as FCC_L12 high entropy alloys, i represents 1a or 3c; for BCC_B2 high entropy alloys, i represents 1a or 1b; and for HCP_D0 19 The high entropy alloy of the structure, i represents 2c or 6h; then use the for loop, set the random number seed 42, shuffle the atomic position coordinates on various sublattices and randomly distribute them; then fill in the element symbol and the number of atoms on the sublattice in turn before the position coordinate list, and you can get the position coordinate allocation structure file POSCAR_Si corresponding to the different types of constituent atoms on the sublattice.

6. The method for constructing a high entropy alloy structure file based on atomic preferential occupancy characteristics according to claim 1, characterized in that: In step S4, TTT mark is filled in the end of the coordinates of various sublattices in the supercell, and the origin TTT with coordinate 000 is modified to FFF; in addition, an atom is selected on the x-axis, y-axis and z-axis respectively, and the TTT after the coordinate is marked as FTT, TFT and TTF accordingly. In order to perform selective dynamics manipulation when relaxing the crystal structure using the first-principles calculation method later, the atomic positions on various sublattices in the supercell are randomly shuffled in step S5 to generate a template file for random arrangement of sublattice positions.

7. The method for constructing a high entropy alloy structure file based on atomic preferential occupancy characteristics according to claim 1, characterized in that: Step S7 classifies and merges the coordinates and atomic numbers of similar atoms; based on the coordinates of different atoms on various sublattices, the structure file POSCAR_Si is allocated, and the coordinates and atomic numbers of similar atoms are classified and merged in alphabetical order of the element symbols to form the structure file POSCAR of the high entropy alloy; wherein the initial basis vector adopts the supercell initial basis vector after the supercell is generated for the prototype structure file.

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