CN115910237A - Python-based atomic coordination number automatic batch calculation statistical method - Google Patents

Abstract

The invention provides an automatic batch calculation statistical method for atomic coordination numbers based on Python, which is to find out a short-range ordered structure from a long-range ordered structure, and perform quantification and imaging characterization. The automatic batch calculation statistical method for realizing the coordination number and the atomic coordinate of the same kind of atoms based on Python is used for researching the local ordered structure of the alloy with complex components, and comprises the following steps: s1, selecting an alloy system and acquiring an atom occupying fraction; s2, building a super cell and an ordered distribution configuration file; s3, a statistical information configuration module; s4, an atom classification generation module; s5, an inter-atom distance calculation file generation module; s6, counting various coordination number file generation modules of atoms of the same type; s7, outputting the file modules in batches; and S8, a visualization module.

Description

Python-based atomic coordination number automatic batch calculation statistical method

Technical Field

The invention relates to the technical field of metal material calculation and simulation, in particular to an automatic batch calculation statistical method for atomic coordination numbers based on Python.

Background

The metal material is closely related to the development of the human society, and the use history is accompanied with the development of the human society, and the use history is more than 3000 years. With the development and demand of science and technology and the increase of international competition, the research and development of novel high-performance metal materials are in need. The conventional metal materials are mainly composed of one or two metal elements, and different alloy elements are added to produce different alloys, such as aluminum alloy mainly composed of Al, steel material mainly composed of Fe, titanium alloy mainly composed of Ti, etc., or TiAl and intermetallic compounds with nearly equal ratio of Ti and Al. However, the traditional alloy system is completely excavated, and the limitation of the traditional alloy design is needed to be broken through urgently. In the last 20 years, the high-entropy alloy and the high-entropy material derived from the high-entropy alloy break through the design concept of the traditional material on components and structures, the research heat tide is started, and a plurality of high-entropy alloys, namely high-entropy material systems show unique microstructure characteristics and special properties and have potential application values. Unlike conventional alloy design concepts, high entropy alloys contain at least five equal or nearly equal atomic percentages of major elements, with no significant difference between solute and solvent. The professor team of leaf-school motherwort of Qinghua university in Taiwan in 2004 and the professor team of Cantor university in Cambridge England simultaneously and independently propose the design concepts of the high-entropy alloy and the equimolar multi-principal-element alloy, the design concepts are basically similar in essence on the design concept of the alloy components, the great ideas stimulate the great interest and practice of material scientists, and the material scientists provide the relationship between the formation mechanism and the structure and the performance of the high-entropy alloy, particularly the four effects of the high-entropy alloy for the early-stage material scientists, namely: (1) a thermodynamically high entropy effect; (2) a kinetically delayed diffusion effect; (3) lattice distortion effects on the structure; (4) The cocktail effect on performance actively carries out rational and quantitative research. Some researchers have also sought in reverse, based on application needs, high-entropy alloy compositions, structures and corresponding manufacturing processes that can meet performance requirements. The basic research and the application development of high-entropy materials including high-entropy alloys are remarkably advanced. Some high-entropy alloy systems have potential application values in many fields due to excellent performances of high strength, fatigue resistance, fracture resistance, thermal stability, high elongation, irradiation resistance and the like. At present, a small amount of alloy systems are applied to metallurgical materials, catalysts, magnetic materials, nuclear materials and the like, and have been subjected to initial light.

Nevertheless, the research and development of the high-entropy alloy have very weak theoretical basis, and a lot of wars and unexplained points are still provided for the high-entropy alloy theory and the method. For example, many researchers challenge the four major effects of the high-entropy alloy and even directly give the contrary example of the inconsistency. The main reason for the disputes is the assumption that there is a lack of quantitative description means.

It is generally believed that high entropy alloys possess stable properties and simple structures due to high entropy of mixing. However, this is based on the fact that atoms occupy randomly on crystal lattice, and the theoretical basis is lacking or even impossible, because the crystal lattices and sublattices of the constituent atom species and different crystal structures are different, so that there must exist a multicomponent solid solution with preferred sites (i.e., site occlusion tendency) mixed randomly, and the ideal configuration entropy is:

wherein R is a gas constant, x _Mi Is the mole fraction of the element Mi and n is the number of components. When x is _M1 ＝x _M2 ＝…＝x _Mn The entropy of the system reaches a maximum. That is, for an isoatomic high entropy alloy system, the configuration entropy can be expressed as:

S _conf ＝Rln(n)

theoretical and experimental results indicate that higher entropy of mixing in the alloy promotes the formation of random solid solution phases with simple structure (e.g. FCC, BCC, HCP) and thus reduces the number of phases. The characteristics of the high-entropy alloy are closely related to the phase structure of the high-entropy alloy, the high-entropy alloy with the FCC phase has high plasticity and lower hardness, and the high-entropy alloy with the BCC phase has high strength and low plasticity. The high-entropy alloy with the mixed phase structure can realize high-strength and high-plasticity combination. In the simplest description, the local chemical environment of a high entropy alloy may be considered to represent a random distribution of different kinds of atoms at lattice positions, i.e. maximum conformational entropy states. <xnotran> , CrFeMnNi , XX ( Cr-Cr, mn-Mn, fe-Fe Ni-Ni) XY ( Cr-Mn, cr-Fe, cr-Ni, mn-Fe, mn-Ni, Fe-Ni). </xnotran> With the continuous and intensive research on high-entropy alloy systems, a small number of scholars find that local order exists in the alloy and is considered to be closely related to the performance of the alloy, but the local order structure is considered to be a password which is difficult to decipher and is not controversial.

Through research in the field of high-entropy alloys for more than 10 years, on one hand, in consideration of the diversity of the types and contents of the components in the high-entropy alloys, the atomic radius, the extra-nuclear electronic structure, the electronegativity of the components atoms and the bonding energy among different atoms in the high-entropy alloys, and some of the components are even different from each other or have obvious difference,

on the other hand, considering that the FCC, BCC or HCP alloy phases all have a well-defined sub-lattice structure, it is believed that the alloy atoms must have either a strong or weak tendency to occupy sites on the sub-lattice, and cannot be completely randomly distributed. Aiming at the complex mechanism of the alloying process, the quantitative internal relation between four elements of alloy composition, process, structure and performance is sought to be established

There must be some tendency for occupancy (i.e., unequal ability to compete for positions on different sublattices), with some atoms tending to occupy one sublattice and some atoms tending to occupy another sublattice, so-called occupancy ordering behavior. Earlier workers, the inventors have patented a quantitative prediction method based on high-entropy alloy atom-occupying ordered behavior, and have granted: ZL 2021100207115 is a calculation method of a high-entropy alloy configuration entropy based on atom occupation ordering behavior. On the basis, when a high-entropy alloy with a given component reaches phase equilibrium at a certain heat treatment temperature, the predicted occupied fraction is calculated to construct a super cell, then atoms are distributed, the coordination situation of the atoms is counted, the statistical rule of the coordination numbers of atoms of the same kind is given, and the statistical rule is displayed in an imaging mode, so that the quantity and the distribution of local ordered structures in a long-range ordered structure described by the occupied fraction are found, the research on the high-entropy alloy is advanced, a solid fine structure foundation is laid for performance regulation, the rational, quantitative and imaging researches on the high-entropy alloy are realized, and the research progress of a new high-entropy alloy material is promoted. The invention not only describes the occupying behavior of different atoms on different sub-lattices quantitatively, but also can further display the atom distribution configuration in an imaging manner, and obtains the local ordered structure of the atoms when the heat treatment reaches the equilibrium state, thereby solving the problem that the difference of the atom types and the alloy phase structure is not considered in the prior literature, and completely adopting an ideal mixing method to quantitatively calculate the high-entropy effect rough method, and the defect that the study on the local ordered structure is not good.

Disclosure of Invention

In view of the above, the present invention aims to provide a method for extracting homogeneous atom coordination information from a complex high-entropy alloy configuration with space-occupying ordered features, that is, finding out a short-range ordered structure from a long-range ordered structure, and quantifying and imaging the structure. And realizing an automated batch calculation statistical method of the coordination number and the atomic coordinate of the same kind of atoms based on Python, and being used for researching the local ordered structure of the alloy with complex components.

In order to realize the purpose, the invention adopts the following technical scheme: an automated atomic coordination number batch calculation statistical method based on Python comprises the following steps:

s1, selecting an alloy system and acquiring an atomic occupying fraction: establishing a suitably sized super cell of FCC, BCC or HCP prototype structure for the position occupancy distribution of atoms;

s2, building a super cell and ordered distribution configuration file: according to the calculated occupation fraction data of each type of alloy atoms on different sub-lattices in the alloy phase by phase balance prediction, carrying out conversion and rounding of various types of atom numbers on various types of sub-lattices of the corresponding size of the super-crystal cell;

step S3, a statistical information configuration module: configuring a calculating element species, a first neighbor atom distance, a base vector length, and an atom coordination number;

step S4, an atom classification generation module: extracting three-dimensional coordinate data of various elements from the POSCAR file, and respectively storing the three-dimensional coordinate data of various elements into an atomic coordinate Excel file;

s5, an interatomic distance calculation file generation module: calculating the distance between every two atoms, and storing atom information which accords with the distance between the first adjacent atoms into a first adjacent Excel file of the atoms, wherein the atom information comprises three-dimensional coordinate data, atom types, atom sequence numbers and atom distances;

s6, counting various file generation modules of the same type of atomic coordination numbers: according to the coordination numbers of atoms of the same type, counting the coordination pairs of each atom under the specified coordination number, and outputting coordination data information in an atom coordination csv file;

step S7, a batch output file module: controlling batch calculation of the same kind of atom coordination pairs of different element types under different coordination numbers;

step S8, a visualization module: collecting and summarizing the same kind of atom coordinate data with the designated coordination number and more than the coordination pair number into a cluster structure file, and applying the initial basis vector of the POSCAR of the super cell to the corresponding position of the same kind of atom cluster POSCAR, wherein the low coordination pair number actually contains the high coordination pair number, namely the atom information in the high coordination pair number is a subset of the atom set in the low coordination pair number; and then performing visual expression on corresponding similar atom clusters by adopting a VESTA software package.

In a preferred embodiment, in step S5, a pandas library is used to convert the atomic coordinate Excel file into DataFrame and list, and output the atomic first neighbor Excel file.

In a preferred embodiment, the statistical atomic coordination number file generation module in step S6 includes the following steps:

step S61: calculating the occurrence frequency of each atom, and storing the key value atom serial number and the value atom occurrence frequency into a dictionary;

step S62: screening out key value atom sequence numbers which accord with coordination numbers from a dictionary, and storing by using a list;

step S63: and using the atom sequence number in the list as an index, finding the data row where the atom is located, and recording the data row into an atom coordination csv file.

The invention has the following beneficial effects:

the method is based on the Python technology, calculates the interatomic distance values of a large number of different elements by using a Python library, performs classification statistics according to different coordination numbers to obtain atom information meeting the coordination number condition, further provides quantitative calculation for researching the space occupying ordering behavior of atoms, and has important guiding significance for accurately predicting the phase formation mechanism and microstructure of the high-entropy alloy.

The calculation process strictly follows the crystallographic structure information of the alloy phase and the thermodynamic theory of the alloy, the quantitative calculation is carried out on the atomic coordination number of the high-entropy alloy, and the formation mechanism of the high-entropy alloy phase can be researched fundamentally and quantitatively, so that a universal calculation method is formed. The method saves the research cost, improves the research efficiency, provides a new thought for further quantitative research of other effects of the high-entropy alloy, and lays a solid fine structure foundation, thereby accelerating the research and development of new high-entropy alloy materials.

Drawings

FIG. 1 is a block flow diagram of a preferred embodiment of the present invention

FIG. 2 is a diagram of the sub-lattice nested unit cells of the ordered FCC _ VCoNi of VCoNi in a preferred embodiment of the present invention

FIG. 3 is an atomic diagram of 20X20 supercell process of V and Co of VCoNi

FIG. 4 is a 20X20 supercell construction diagram of VCoNi in accordance with a preferred embodiment of the present invention

FIG. 5 is a fragment diagram of all atomic POSCAR files for VCoNi in accordance with a preferred embodiment of the present invention

FIG. 6 shows the distribution of each atom of VCoNi in FCC crystal lattice according to a preferred embodiment of the present invention

FIG. 7 is a fragment diagram of the V atom POSCAR file of VCoNi in the preferred embodiment of the present invention

FIG. 8 is a fragment diagram of the Co atom POSCAR file of VCoNi in the preferred embodiment of the present invention

FIG. 9 is a fragment diagram of the Ni atom POSCAR file of VCoNi in the preferred embodiment of the present invention

FIG. 10 is a block diagram of information configuration of VCoNi in accordance with the preferred embodiment of the present invention

FIG. 11 is a diagram of preferred embodiment of the present invention of M x 8M coordinated clusters of atoms of VCoNi

FIG. 12 is a diagram of clusters of atoms of the same type with random occupation of VCoNi, M.times. -8M and coordination above M.times. -8M in accordance with a preferred embodiment of the present invention

FIG. 13 is a diagram of clusters of atoms of the same type coordinated by VCoNi at M x-7M and M x-7M in preferred positions according to the preferred embodiment of the present invention

FIG. 14 is a diagram of homogeneous clusters of atoms coordinated by M-7M and M-7M with random site occupation in VCoNi according to the preferred embodiment of the present invention

Detailed Description

The invention is further explained by the following embodiments in conjunction with the drawings.

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application; as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

An automated statistical method for batch calculation of atomic coordination number based on Python, referring to FIGS. 1 to 14, includes the following steps:

s1, selecting an alloy system and acquiring an atomic occupying fraction: establishing a suitably sized super cell of FCC, BCC or HCP prototype structure for the position occupancy distribution of atoms;

s2, building a super cell and ordered distribution configuration file: and according to the calculated occupation fraction data of each type of alloy atoms on different sub-lattices in the alloy phase by phase balance prediction, carrying out conversion and rounding of various types of atomic numbers on various types of sub-lattices of the corresponding size of the super-crystal cell.

Step S3, a statistical information configuration module: configuring a calculating element species, a first neighbor atom distance, a base vector length, and an atom coordination number;

step S4, an atom classification generation module: extracting three-dimensional coordinate data of various elements from the POSCAR file, and respectively storing the three-dimensional coordinate data of various elements into an atomic coordinate Excel file;

s5, an interatomic distance calculation file generation module: calculating the distance between every two atoms, and storing atom information which accords with the distance between the first adjacent atoms into a first adjacent Excel file of the atoms, wherein the atom information comprises three-dimensional coordinate data, atom types, atom sequence numbers and atom distances;

s6, counting various atom coordination number file generation modules of the same type: according to the coordination numbers of atoms of the same type, counting the coordination pairs of each atom under the specified coordination number, and outputting coordination data information in an atom coordination csv file;

step S7, batch output file module: controlling to calculate the same kind atom coordination pairs of different element types under different coordination numbers in batch;

step S8, a visualization module: collecting and summarizing the same kind of atom coordinate data with the designated coordination number and more than the coordination logarithm into a cluster structure file, and applying the initial basis vector of the POSCAR of the super cell to the corresponding position of the same kind of atom cluster POSCAR. And then performing visual expression on corresponding similar atom clusters by adopting a VESTA software package.

In step S5, a pandas library is used to convert the atomic coordinate Excel file into DataFrame and list, and output the atomic first neighbor Excel file.

The statistical atomic coordination number file generation module in the step S6 comprises the following steps:

step S61: calculating the occurrence times of every two atoms, and storing the occurrence times of the key value atom sequence number and the value atom into a dictionary;

step S62: screening out key value atom sequence numbers which accord with coordination numbers from a dictionary, and storing by using a list;

step S63: and using the atom sequence number in the list as an index, finding the data row where the atom is located, and recording the data row into an atom coordination csv file.

In this embodiment, taking VCoNi multi-principal component alloy (also known as a medium entropy alloy or a high entropy alloy) with FCC structure as an example, an automated batch statistical method for atomic coordination number based on Python is provided, which includes the following steps:

s1, selecting a VCoNi alloy system with an FCC structure and obtaining an atomic occupying fraction, wherein the alloy is unique, the occupying behavior is not influenced by temperature, the occupying is constant and ordered, and the theoretical prediction result is consistent with the experimental report result. While the occupancy behavior of other alloy systems may change with temperature.

S2, building a super cell and ordered distribution configuration file: and according to the calculated occupation fraction data of each type of alloy atoms on different sub-lattices in the alloy phase by phase balance prediction, carrying out conversion and rounding of various types of atomic numbers on various types of sub-lattices of the corresponding size of the super-crystal cell. The details of the space occupation and the visualization are shown in fig. 2. This embodiment establishes a 20x20x20 super cell of the FCC prototype structure of VCoNi for the position occupancy distribution of atoms based on existing computational resources.

The formatted POSCAR file fragment is shown in FIG. 5. And stripping various atoms in the 20X20 CoNiV _FCCMPAsuper cell, making an imaging file and a data file, namely extracting three-dimensional coordinate information in the POSCAR file, and storing the three-dimensional coordinate information in the Excel files corresponding to V, co and Ni for further analysis.

Formatted POSCAR document fragment diagrams with various atoms stripped out are shown in FIGS. 7-9.

And step S3: according to a similar idea of constructing a 20 × 20 × 20 super cell, a 3 × 3 × 3 super cell of the structure is established, cell volume optimization is performed, and the first adjacent atom distance is obtained

And step S4: setting the element types as V, co and Ni through a configuration module, and obtaining a first adjacent atomic distance as V, co and Ni based on the optimization of the unit cell volume of the 3 multiplied by 3 super unit cell in the step S3

The superlattice cell constant is->

The first neighbor atom distance which leads out of a 20X20 supercell is still +>

Base vector length of>

Then, the coordination information (namely the first adjacent atom) of the same kind of atoms of various atoms is collected and searched, and the coordination number is set as: 1. 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, wherein 12 is the first nearest neighbor atomic number maximum in the FCC pure element unit cell.

Step S5: and traversing and calculating the distance between every two atoms in each type of atoms through an atom distance file generation module, and storing the data of the atoms meeting the first adjacent atom distance into an atom first adjacent Excel file, wherein the data comprises three-dimensional coordinate data, atom types, atom sequence numbers and atom distances.

Step S6: through an atom coordination number file generation module, atom sequences meeting the conditions when the coordination numbers are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12 are counted respectively, and atom coordination csv files meeting the coordination number are output.

As shown in table 1, it can be seen that the higher the coordination number n is, the smaller the number of coordination clusters (Group numbers) is, and the number of coordination clusters is significantly greater in the assumed state of random occupancy, but this assumption is not consistent with the significant occupancy ordering tendency of different atoms in this system, and can only be used as a comparison result. In general, when the heat treatment reaches the equilibrium state, the alloy has the tendency of occupying space and ordering, and the maximum coordination number does not exceed 8. The similar atom coordination conditions of other alloy systems can be statistically analyzed according to the steps:

TABLE 1

And S7, counting the probability of cluster formation of atoms of the same type under the specified coordination number, and representing local ordering characteristics.

For atoms of the same type, the probability calculation formula with 8 coordination is as follows:

P(M*-8M)＝[N _g ×(1+8)/10667]×1000‰＝N _g ×0.843‰, (1)

P(V*-8V)＝N _g ×0.843‰＝40×0.843‰＝33.72‰,

P(Co*-8Co)＝N _g ×0.843‰＝8×0.843‰＝6.74‰,

P(Ni*-8Ni)＝N _g ×0.843‰＝8×0.843‰＝6.74‰.

and S8, expressing the coordination cluster in an imaging mode. Next, the atom clusters with the same kind of atoms assigned coordination numbers are expressed in an imaging manner, and the cluster configuration with high coordination numbers among them also satisfies the requirement of the cluster with low coordination numbers, so the image of the cluster with low coordination numbers also includes information of high coordination numbers. Fig. 11 is a graph of clusters of atoms of the same type coordinated by M x-M8 and M x-M8 above in the preferred space occupation of VCoNi, fig. 12 is a graph of clusters of atoms of the same type coordinated by M x-M8 and M x-M8 above in the random space occupation of VCoNi, fig. 13 is a graph of clusters of atoms of the same type coordinated by M x-M7 and M x-M7 above in the preferred space occupation of VCoNi, and fig. 14 is a graph of clusters of atoms of the same type coordinated by M x-M7 and M x-M7 above in the random space occupation of VCoNi.

The above description is only an example of the present invention, and all the equivalent changes and modifications made according to the claims of the present invention should be covered by the present invention.

Claims (3)

1. An automated atomic coordination number batch calculation statistical method based on Python is characterized by comprising the following steps:

s1, selecting an alloy system and obtaining an atomic occupying fraction: establishing a suitably sized super cell of FCC, BCC or HCP prototype structure for the position occupancy distribution of atoms;

s2, building super cells and an ordered distribution configuration file: according to the calculated occupation fraction data of each type of alloy atoms on different sub-lattices in the alloy phase by phase balance prediction, carrying out conversion and rounding of various types of atom numbers on various types of sub-lattices of the corresponding size of the super-crystal cell;

step S3, a statistical information configuration module: calculating element species, first neighbor atomic distance, basis vector length and atomic coordination number;

step S4, an atom classification generation module: extracting three-dimensional coordinate data of various elements from the POSCAR file, and respectively storing the three-dimensional coordinate data of various elements into an atomic coordinate Excel file;

s5, an interatomic distance calculation file generation module: calculating the distance between every two atoms, and storing atom information which accords with the distance of the first adjacent atom into a first adjacent Excel file of the atom, wherein the atom information comprises three-dimensional coordinate data, atom types, atom sequence numbers and atom distances;

s6, counting various atom coordination number file generation modules of the same type: according to the coordination numbers of atoms of the same type, counting the coordination pairs of each atom under the specified coordination number, and outputting coordination data information in an atom coordination csv file;

step S7, batch output file module: controlling to calculate the same kind atom coordination pairs of different element types under different coordination numbers in batch;

step S8, a visualization module: collecting and summarizing the same kind of atom coordinate data with the designated coordination number and more than the coordination pair number into a cluster structure file, and applying the initial basis vector of the POSCAR of the super cell to the corresponding position of the same kind of atom cluster POSCAR, wherein the low coordination pair number actually contains the high coordination pair number, namely the atom information in the high coordination pair number is a subset of the atom set in the low coordination pair number; and then performing visual expression on corresponding similar atom clusters by adopting a VESTA software package.

2. The automated Python-based atomic coordination number batch computation statistical method according to claim 1, wherein in step S5, a pandas library is used to convert an atomic coordinate Excel file into DataFrame and list, and output an atomic first neighbor Excel file.

3. The automated statistical method for batch calculation of atomic coordination numbers based on Python according to claim 1, wherein the statistical atomic coordination number file generation module in step S6 comprises the following steps:

step S61: calculating the occurrence frequency of each atom, and storing the key value atom serial number and the value atom occurrence frequency into a dictionary;

step S62: screening out key value atom sequence numbers which accord with coordination numbers from a dictionary, and storing by using a list;

step S63: and using the atom sequence number in the list as an index, finding the data row where the atom is located, and recording the data row into an atom coordination csv file.

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