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Prediction of Mechanical Properties of High-Entropy Carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning Potential. / Pikalova, N.; Balyakin, I.; Yuryev, A. et al.
In: Doklady Physical Chemistry, Vol. 514, No. 1, 2024, p. 9-14.

Research output: Contribution to journal › Article › peer-review

Harvard

Pikalova, N, Balyakin, I, Yuryev, A & Rempel, A 2024, 'Prediction of Mechanical Properties of High-Entropy Carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning Potential', Doklady Physical Chemistry, vol. 514, no. 1, pp. 9-14. https://doi.org/10.1134/S0012501624600049

APA

Pikalova, N., Balyakin, I., Yuryev, A., & Rempel, A. (2024). Prediction of Mechanical Properties of High-Entropy Carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning Potential. Doklady Physical Chemistry, 514(1), 9-14. https://doi.org/10.1134/S0012501624600049

Vancouver

Pikalova N, Balyakin I, Yuryev A, Rempel A. Prediction of Mechanical Properties of High-Entropy Carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning Potential. Doklady Physical Chemistry. 2024;514(1):9-14. doi: 10.1134/S0012501624600049

Author

Pikalova, N. ; Balyakin, I. ; Yuryev, A. et al. / Prediction of Mechanical Properties of High-Entropy Carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning Potential. In: Doklady Physical Chemistry. 2024 ; Vol. 514, No. 1. pp. 9-14.

BibTeX

@article{5f7446fb28f94597a99ed83dfc41ad5f,
title = "Prediction of Mechanical Properties of High-Entropy Carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning Potential",
abstract = "The six-component high-entropy carbide (HEC) (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C has been studied. The electronic structure was calculated using the ab initio VASP package for a 512-atom supercell constructed with the use of special quasi-random structures. The artificial neural network potential (ANN potential) was obtained by deep machine learning. The quality of the ANN potential was estimated by standard deviations of energies, forces, and virials. The generated ANN potential was used in the LAMMPS classical molecular dynamics software to analyze both the defect-free model of the alloy comprising 4096 atoms and, for the first time, the model of the polycrystalline HEC composed of 4603 atoms. Simulation of uniaxial cell tension was carried out, and elastic coefficients, bulk modulus, elastic modulus, and Poisson{\textquoteright}s ratio were determined. The obtained values are in good agreement with experimental and calculated data, which indicates a good predictive ability of the generated ANN potential.",
author = "N. Pikalova and I. Balyakin and A. Yuryev and A. Rempel",
year = "2024",
doi = "10.1134/S0012501624600049",
language = "English",
volume = "514",
pages = "9--14",
journal = "Doklady Physical Chemistry",
issn = "0012-5016",
publisher = "Maik Nauka-Interperiodica Publishing",
number = "1",

}

RIS

TY - JOUR

T1 - Prediction of Mechanical Properties of High-Entropy Carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning Potential

AU - Pikalova, N.

AU - Balyakin, I.

AU - Yuryev, A.

AU - Rempel, A.

PY - 2024

Y1 - 2024

N2 - The six-component high-entropy carbide (HEC) (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C has been studied. The electronic structure was calculated using the ab initio VASP package for a 512-atom supercell constructed with the use of special quasi-random structures. The artificial neural network potential (ANN potential) was obtained by deep machine learning. The quality of the ANN potential was estimated by standard deviations of energies, forces, and virials. The generated ANN potential was used in the LAMMPS classical molecular dynamics software to analyze both the defect-free model of the alloy comprising 4096 atoms and, for the first time, the model of the polycrystalline HEC composed of 4603 atoms. Simulation of uniaxial cell tension was carried out, and elastic coefficients, bulk modulus, elastic modulus, and Poisson’s ratio were determined. The obtained values are in good agreement with experimental and calculated data, which indicates a good predictive ability of the generated ANN potential.

AB - The six-component high-entropy carbide (HEC) (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C has been studied. The electronic structure was calculated using the ab initio VASP package for a 512-atom supercell constructed with the use of special quasi-random structures. The artificial neural network potential (ANN potential) was obtained by deep machine learning. The quality of the ANN potential was estimated by standard deviations of energies, forces, and virials. The generated ANN potential was used in the LAMMPS classical molecular dynamics software to analyze both the defect-free model of the alloy comprising 4096 atoms and, for the first time, the model of the polycrystalline HEC composed of 4603 atoms. Simulation of uniaxial cell tension was carried out, and elastic coefficients, bulk modulus, elastic modulus, and Poisson’s ratio were determined. The obtained values are in good agreement with experimental and calculated data, which indicates a good predictive ability of the generated ANN potential.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85190296684

UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=001201675000002

U2 - 10.1134/S0012501624600049

DO - 10.1134/S0012501624600049

M3 - Article

VL - 514

SP - 9

EP - 14

JO - Doklady Physical Chemistry

JF - Doklady Physical Chemistry

SN - 0012-5016

IS - 1

ER -

ID: 55695002