High-entropy alloy component optimization design method, computer equipment and storage medium

Abstract

The invention discloses a molecular dynamics simulation-based high-entropy alloy component optimization design method, which comprises the following steps of: constructing nano polycrystalline high-entropy alloy models with different components, calculating stacking fault energy of the nano polycrystalline high-entropy alloy models, drawing a relational graph fitting the stacking fault energy and the components, determining the component with the minimum stacking fault energy as the optimized high-entropy alloy component, and determining the component with the minimum stacking fault energy as the optimized high-entropy alloy component. And the corresponding flow stress of the high-entropy alloy is the maximum. According to the method, cross-scale optimization from component design to performance prediction is realized by utilizing quantitative correlation between an atomic scale defect evolution mechanism and stacking fault energy.

Description

Technical Field

[0001] The present invention relates to the fields of computational materials science and metal material design, and specifically to a design method for nano-polycrystalline high-entropy alloys (HEAs) guided by molecular dynamics simulation for composition regulation, as well as a composition optimization model based on this method. The method is particularly suitable for the composition design of high-strength and high-toughness structural materials in extreme environments. Background Art

[0002] Existing high-entropy alloy composition design primarily relies on experimental trial-and-error or data-driven approaches. Literature reports (e.g., Bracq et al. in Acta Materialia 2019, 177:266-279) require multiple rounds of experiments and testing, resulting in a long R&D cycle. Machine learning-based screening methods (e.g., CN115579091A) rely heavily on large datasets and lack sufficient explanation of physical mechanisms, providing only black-box composition recommendations. In summary, the traditional trial-and-error approach and data-driven models struggle to balance efficiency and mechanistic clarity, becoming a key technical obstacle to the rational design of high-entropy alloys. Summary of the Invention

[0003] In response to the above-mentioned technical problems and the shortcomings in the field, the present invention provides a high-entropy alloy composition optimization design method based on molecular dynamics simulation. This method utilizes the quantitative correlation between the atomic-scale defect evolution mechanism and the stacking fault energy (SFE) and stacking fault density (SFs) to achieve cross-scale optimization from composition design to performance prediction.

[0004] A method for optimizing the composition of high-entropy alloys based on molecular dynamics simulations comprises: constructing nano-polycrystalline high-entropy alloy models of different compositions and calculating their stacking fault energies, drawing a fitted diagram of the relationship between the stacking fault energy and the composition, and determining the composition with the smallest stacking fault energy as the optimized high-entropy alloy composition, which corresponds to the maximum flow stress of the high-entropy alloy.

[0005] In some embodiments, in the high entropy alloy composition optimization design method based on molecular dynamics simulation, the nano-polycrystalline high entropy alloy model is an FCC structure.

[0006] In some embodiments, the high entropy alloy composition optimization design method based on molecular dynamics simulation, the nano-polycrystalline high entropy alloy model is an FCC structure, a CoCrFeMnNi series high entropy alloy model, and further, the expression in terms of atomic ratio is Co x Cr 40-x Fe 20 Mn 20 Ni 20 In the high entropy alloy model, x is 0 to 40, further 10 to 25, and further 12 to 20. An exemplary optimal composition may be x=18, which corresponds to Co18 Cr 22 Fe 20 Mn 20 Ni 20 High entropy alloys have the largest flow stress.

[0007] In some embodiments, the high entropy alloy composition optimization design method based on molecular dynamics simulation uses the Voronoi algorithm (or equivalent software, such as Atomsk) to construct a nano-polycrystalline high entropy alloy model, the overall size of the model is in the order of tens of cubic nanometers, and the total number of atoms is 10 5 Order of magnitude, the number of grains is set according to the target grain size;

[0008] A potential function based on the Lennard-Jones many-body potential was selected, in which the cross-interaction parameters of heterogeneous atoms were determined using the arithmetic average method, and the cutoff distance was set within the range of the fifth to sixth nearest atomic distance. The parameters of this potential function were obtained by fitting the mixing enthalpy, atomic pair binding energy and atomic radius data of the high-entropy alloy system, which is essentially different from the existing technical solution for calculating elastic constants using VASP software.

[0009] This potential function was chosen for its excellent mechanical parameter performance at room temperature. Furthermore, its use significantly improved sample processing efficiency, enabled the selection of smaller grain size intervals, and enabled reproducible testing of grain size effects.

[0010] In some embodiments, in the high entropy alloy composition optimization design method based on molecular dynamics simulation, the stacking fault energy SFE is calculated by using LAMMPS according to the following formula:

[0011]

[0012] Where ΔE is the stacking fault formation energy and A is the stacking fault area.

[0013] The method of the present invention can also involve cross-scale fusion and mechanism analysis: through cross-scale simulation, it can reveal the impact of composition changes on grain boundary defects and stacking fault networks, as well as the synergistic strengthening mechanism of these microstructures on the mechanical properties of materials.

[0014] The method of the present invention reveals the synergistic strengthening mechanism of "composition-grain boundary defects-stacking fault network-flow stress" in nano-polycrystalline high-entropy alloys, providing a theoretical basis for the composition design of high-performance structural materials under extreme environments.

[0015] The method of the present invention has the following advantages:

[0016] 1. The study clearly reveals the "U-shaped" regulation of SFE by the mismatch between electron concentration and atomic size in alloys, breaking the black-box data-driven model;

[0017] 2. By fitting the potential function parameters, high-precision simulation can be achieved at a low computational cost;

[0018] 3. The simulation results have good repeatability, which significantly shortens the component design cycle.

[0019] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory. When the computer program is run, the processor executes the high entropy alloy composition optimization design method based on molecular dynamics simulation.

[0020] The present invention also provides a computer-readable storage medium, which stores a program or instruction. When the program or instruction is executed by a computer device, the computer device executes the high-entropy alloy composition optimization design method based on molecular dynamics simulation.

[0021] As a general inventive concept, the present invention also provides a nano-polycrystalline high entropy alloy, the chemical expression of which is Co 18 Cr 22 Fe 20 Mn 20 Ni 20 , which has high strength and toughness.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] Design efficiency: Compared with traditional trial-and-error methods or data-driven models, the method of the present invention can significantly shorten the component design cycle and reduce computational costs while taking into account the explanatory power of physical mechanisms.

[0024] Mechanistic Clarity: Through cross-scale simulation methods, the intrinsic regulatory mechanism of composition on stacking fault energy, defect evolution, and mechanical response is revealed, providing a scientific and efficient optimization approach for high-entropy alloy composition design, avoiding the opacity of black-box data-driven design.

[0025] Wide range of applications: This method is not only applicable to the CoCrFeMnNi system, but can also be extended to the design of other multi-component high-entropy alloys after appropriate parameter adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Schematic diagram of the molecular dynamics simulation process in a specific implementation manner.

[0027] Figure 2 Graph showing the correlation between the Co atomic ratio and SFE (a), stacking fault density, and flow stress (b) in a specific embodiment.

[0028] Figure 3 In the figure, (a1)-(a4) are the microstructure evolution diagrams of 18at.% Co HEA specimens under different strains; (b) snapshot of the nucleation of Shockley partial dislocations at the internal grain boundaries of 18at.% Co HEA specimens; (c) snapshot of the interaction between dislocations and stacking faults in 18at.% Co HEA specimens; (d) snapshot of disordered atoms in 18at.% Co HEA specimens as new dislocation sources. DETAILED DESCRIPTION

[0029] The present invention will be further described below with reference to the accompanying drawings and specific examples. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0030] Example 1:

[0031] Figure 1 The flowchart of the stacking fault energy (SFE)-based molecular dynamics simulation-based optimization design method for CoCrFeMnNi high-entropy alloy composition is shown, which specifically includes the following steps:

[0032] Step 1: Polycrystalline model construction and parameter setting:

[0033] (1) Polycrystalline model construction:

[0034] Use Atomsk's Voronoi algorithm (or other equivalent software) to build a pure FCC structure Ni nanopolycrystalline model with a model size of 18nm 3 The grain size is 18.4nm and the total number of atoms is about 500,000. Co is generated by randomly replacing Ni atoms in an equiatomic ratio using LAMMPS software. x Cr 40-x Fe 20 Mn 20 Ni 20 Alloy models, with x values ​​ranging from 0 to 40, all have equiaxed crystal structures. Model generation commands are integrated into Python scripts for batch processing.

[0035] (2) Parameter settings:

[0036] In LAMMPS, metal units were used, three-dimensional periodic boundary conditions were set, the nearest neighbor parameter was 0.3 nm, and the integration time step was 1 fs. The NPT ensemble temperature was controlled using the Nose-Hoover algorithm, with a pressure relaxation time of 100 fs and a temperature fluctuation standard deviation of <0.5 K.

[0037] (3) Setting of potential function:

[0038] In molecular dynamics software such as LAMMPS, a potential function based on the Lennard-Jones many-body potential is used. Its parameters are fitted based on the mixing enthalpy, atom-pair binding energy, and atomic radius data of the CoCrFeMnNi system. The specific process includes:

[0039] ① Establish the initial parameter matrix based on Cantor alloy mixing enthalpy;

[0040] ②Verify the cross-interaction parameters through atomic pair binding energy;

[0041] ③ The cutoff distance was optimized using the energy minimization module (error < 0.8%). The arithmetic mean of the heterogeneous atomic crossover parameters was taken, and the cutoff distance was set to the fifth to sixth nearest neighbor atomic distance.

[0042] Step 2: Loading conditions and performance simulation:

[0043] (1) Structural optimization and loading: After optimizing the model using the conjugate gradient method, NPT relaxation was performed at 300 K for about 500 ps. Uniaxial stretching was performed along the

[111] crystal direction with a strain rate of 1×10 9 s -1 , total strain 20%, data output interval 0.1% strain. X / Y direction constant pressure zero stress control.

[0044] (2) Output the engineering stress-strain curve using the compute stress / atom command, and save the atomic configuration file (dump format) every 0.5% strain. The thermo command monitors the system temperature, potential energy, and volume changes in real time. The equilibrium state is determined when the kinetic energy fluctuation is less than 5%.

[0045] Step 3: Defect evolution analysis and critical component determination:

[0046] (1) Defect identification and quantification:

[0047] Crystal structure classification: Use the Common Neighbor Analysis (CNA) module of OVITO to distinguish FCC, HCP and amorphous atoms (threshold set to 0.8, matching degree > 90%). The number of HCP structure atoms is used as the number of atoms in the subsequent stacking fault region N. SF .

[0048] Stacking fault density calculation: by local atomic displacement vector (threshold ) to identify the stacking fault region, and the stacking fault density SFs is calculated as follows:

[0049]

[0050] Among them, N SF is the number of atoms in the stacking fault region, N total is the total number of atoms.

[0051] Dislocation network analysis: Dislocation Extraction Algorithm (DXA) was used to extract dislocation line density and Burgers vector, and the proportion of Shockley partial dislocations was calculated.

[0052] To improve efficiency, this module can be integrated into Python scripts to achieve batch extraction of stacking fault density data.

[0053] (2) Critical component determination:

[0054] SFE calculation: Based on the stacking fault energy formula Where ΔE is the stacking fault formation energy, A is the stacking fault area, and the SFE values ​​under different compositions are calculated.

[0055] Inflection point analysis:

[0056] The curves of SFE or SFs changing with Co content were fitted to determine the inflection point (x = 18, 18 at.% CoHEA), and the error analysis (standard deviation of three independent simulations < 0.5 mJ / m 2 )Verify the reliability of the results.

[0057] Step 4: Mechanism analysis of the attached figure:

[0058] Figure 2 (a): The "U-shaped" curve of SFE shows that when Co = 18at.%, the electron concentration (Co: 1.88, Cr: 1.66, Ni: 1.91) and the atomic size mismatch (Δr = 2.52) reach a dynamic equilibrium, forming the lowest stacking fault energy state.

[0059] Figure 2 (b): The “inverted U-shaped” synergistic response of stacking fault density and flow stress confirms that reducing SFE can promote the formation of stacking fault network (peak value 19.12%) and improve the work hardening ability by hindering dislocation movement.

[0060] Figure 3 :Uncovering the dynamic evolution mechanism of defects:

[0061] (a1)-(a4) Microstructure evolution of the specimens under different strains;

[0062] (b) The grain boundary disordered region serves as the preferred nucleation site for Shockley dislocations;

[0063] (c) The stacking fault-dislocation interaction produces a pinning effect;

[0064] (d) Stacking fault intersection induces local disorder, forming a self-catalytic defect proliferation channel.

[0065] Figure 2and Figure 3 The analysis results show that the "U-shaped" evolution of SFE directly regulates the "inverted U-shaped" response of the stacking fault density (SFs) (peak value of 19.12%), and hinders dislocation slip and induces the formation of secondary defect sources through the stacking fault network, significantly improving the work hardening ability of the material. In addition, the grain boundary disordered region serves as the preferential site for the nucleation of Shockley dislocations. Its stress field superposition effect drives the expansion of stacking faults and triggers the self-catalytic defect proliferation channel. The present invention reveals the synergistic strengthening mechanism of "composition-grain boundary defects-stacking fault network" in nano-polycrystalline high-entropy alloys, providing a theoretical basis for the composition design of high-performance structural materials for extreme environments.

[0066] Example 2 (equivalent scheme):

[0067] Based on Example 1, the following parts can be adjusted:

[0068] When constructing the model, different grain numbers or model sizes are used to adapt to the microstructural characteristics of the target material;

[0069] The loading rate and strain amplitude can be adjusted within a certain range according to the experimental or numerical simulation requirements;

[0070] In addition to CNA, other crystal structure determination methods can be used in defect identification, and the matching threshold and displacement threshold can also be appropriately changed;

[0071] The potential function parameters can also be obtained through other fitting methods to ensure that the applicability is not limited to a certain literature data.

[0072] Through the above adjustments, the method of the present invention is not only applicable to the CoCrFeMnNi system, but can also be extended to the composition optimization design of other multi-component high-entropy alloys.

[0073] In summary, the present invention discloses a method for optimizing the composition of high-entropy alloys based on molecular dynamics simulation, which belongs to the field of computational materials science and metal material design. This method quantitatively analyzes the intrinsic relationship between defect evolution and stacking fault energy (SFE) and stacking fault density (SFs) by constructing a nano-polycrystalline atomic model and combining it with a uniaxial compression loading simulation based on the Lennard-Jones multi-body potential. An innovative cross-scale model of "composition-atomic size mismatch (Δr)-SFE or SFs-flow stress" was established, revealing the "U-shaped" response law of SFE synergistically controlled by electron concentration and atomic size. The optimal composition was determined, which significantly improved the computational efficiency, shortened the design cycle, and effectively broke through the limitations of traditional trial-and-error methods and black-box machine learning models. The present invention provides a scientific basis and theoretical guidance for the rational design of high-strength and toughness alloys under extreme environments.

[0074] In addition, it should be understood that after reading the above description of the present invention, those skilled in the art may make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. A high entropy alloy composition optimization design method based on molecular dynamics simulation, characterized in that: include: Nano-polycrystalline high-entropy alloy models with different compositions were constructed and their stacking fault energies were calculated. A relationship diagram between the fitted stacking fault energy and composition was drawn, and the composition with the smallest stacking fault energy was determined as the optimized high-entropy alloy composition, and the corresponding high-entropy alloy had the largest flow stress.

2. The high entropy alloy composition optimization design method based on molecular dynamics simulation according to claim 1, characterized in that: The nano-polycrystalline high entropy alloy model is an FCC structure, which is a CoCrFeMnNi series high entropy alloy model.

3. The high entropy alloy composition optimization design method based on molecular dynamics simulation according to claim 2, characterized in that: The nano-polycrystalline high entropy alloy model is expressed in terms of atomic ratio as Co x Cr 40-x Fe 20 Mn 20 Ni 20 High entropy alloy model, x ranges from 0 to 40.

4. The high entropy alloy composition optimization design method based on molecular dynamics simulation according to claim 3, characterized in that: The value of x ranges from 10 to 25.

5. The high entropy alloy composition optimization design method based on molecular dynamics simulation according to claim 4, characterized in that: The value of x ranges from 12 to 20.

6. The high entropy alloy composition optimization design method based on molecular dynamics simulation according to claim 1, characterized in that: The Voronoi algorithm is used to construct a nano-polycrystalline high entropy alloy model. The overall size of the model is in the order of tens of cubic nanometers, and the total number of atoms is 10 5 Order of magnitude, the number of grains is set according to the target grain size; A potential function based on the Lennard-Jones many-body potential was selected, in which the heterogeneous atomic cross-interaction parameters were determined by the arithmetic average method, and the cutoff distance was set within the range of the fifth to sixth nearest neighbor atomic distance. The potential function parameters were obtained by fitting the mixing enthalpy, atomic pair binding energy and atomic radius data of the high entropy alloy system.

7. The high entropy alloy composition optimization design method based on molecular dynamics simulation according to claim 1, characterized in that: The stacking fault energy SFE is calculated using LAMMPS as follows: Where ΔE is the stacking fault formation energy and A is the stacking fault area.

8. A computer device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, wherein: When the computer program is executed, the processor executes the high entropy alloy composition optimization design method based on molecular dynamics simulation according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program or instruction, and when the program or instruction is executed by a computer device, the computer device executes the high-entropy alloy composition optimization design method based on molecular dynamics simulation according to any one of claims 1 to 7.

10. A nano-polycrystalline high entropy alloy, characterized in that: The chemical expression in terms of atomic ratio is Co 18 Cr 22 Fe 20 Mn 20 Ni 20 .

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