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RAS PresidiumДоклады Российской академии наук. Химия, науки о материалах Doklady Chemistry

  • ISSN (Print) 2686-9535
  • ISSN (Online) 3034-5111

High-entropy carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C mechanical properties prediction with the use of machine learning potential

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PII
10.31857/S2686953524010073-1
DOI
10.31857/S2686953524010073
Publication type
Article
Status
Published
Authors
A. A. Rempel
  • Occupation: Academician of the RAS
  • Affiliation: Institute of Metallurgy, Ural Branch of the Russian Academy of Sciences
Volume/ Edition
Volume 514 / Issue number 1
Pages
65-71
Abstract
The six-component high-entropy carbide (HEC) (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C has been studied. The electronic structure was calculated by using the ab initio package VASP for a supercell with 512 atoms constructed by using special quasi-random structures. The artificial neural networks potential (ANN-potential) was obtained by deep machine learning. The quality of the ANN-potential was estimated by the value of the energies, forces, and virials standard deviations. The generated ANN-potential was used to analyze both a defect-free model of the specified alloy, with 4096 atoms, and for the first time a polycrystalline HEC model, with 4603 atoms, by using the LAMMPS classical molecular dynamics package. The simulation of uniaxial cell tension was carried out, the elasticity coefficients, the all-round compression modulus, the elasticity modulus, and Poisson’s ratio were determined. The obtained values are in good agreement with the experimental and calculated data, which indicates a good predictive ability of the generated ANN-potential.
Keywords
высокоэнтопийная керамика ab initio молекулярная динамика потенциал машинного обучения механические свойства
Date of publication
18.09.2025
Year of publication
2025
Number of purchasers
0
Views
4

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