Open Access
npj Computational Materials, издание 9, том 1, номер публикации 7
Machine learning-driven synthesis of TiZrNbHfTaC5 high-entropy carbide
Pak Alexander Ya
1
,
Sotskov Vadim
2
,
Vassilyeva Yuliya Z
1
,
Bolatova Zhanar S
1
,
Kvashnina Yulia A
3
,
Mamontov Gennady Ya.
1
,
Shapeev Alexander V
2
,
Тип публикации: Journal Article
Дата публикации: 2023-01-13
Springer Nature
Журнал:
npj Computational Materials
Квартиль SCImago: Q1
Квартиль WOS: Q1
Impact factor: 9.7
ISSN: 20573960
Computer Science Applications
General Materials Science
Mechanics of Materials
Modeling and Simulation
Краткое описание
Synthesis of high-entropy carbides (HEC) requires high temperatures that can be provided by electric arc plasma method. However, the formation temperature of a single-phase sample remains unknown. Moreover, under some temperatures multi-phase structures can emerge. In this work, we developed an approach for a controllable synthesis of HEC TiZrNbHfTaC5 based on theoretical and experimental techniques. We used Canonical Monte Carlo (CMC) simulations with the machine learning interatomic potentials to determine the temperature conditions for the formation of single-phase and multi-phase samples. In full agreement with the theory, the single-phase sample, produced with electric arc discharge, was observed at 2000 K. Below 1200 K, the sample decomposed into (Ti-Nb-Ta)C, and a mixture of (Zr-Hf-Ta)C, (Zr-Nb-Hf)C, (Zr-Nb)C, and (Zr-Ta)C. Our results demonstrate the conditions for the formation of HEC and we anticipate that our approach can pave the way towards targeted synthesis of multicomponent materials.
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Pak A. Ya. et al. Machine learning-driven synthesis of TiZrNbHfTaC5 high-entropy carbide // npj Computational Materials. 2023. Vol. 9. No. 1. 7
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Pak A. Ya., Sotskov V., Gumovskaya A. A., Vassilyeva Y. Z., Bolatova Z. S., Kvashnina Y. A., Mamontov G. Y., Shapeev A. V., Kvashnin A. G. Machine learning-driven synthesis of TiZrNbHfTaC5 high-entropy carbide // npj Computational Materials. 2023. Vol. 9. No. 1. 7
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TY - JOUR
DO - 10.1038/s41524-022-00955-9
UR - https://doi.org/10.1038/s41524-022-00955-9
TI - Machine learning-driven synthesis of TiZrNbHfTaC5 high-entropy carbide
T2 - npj Computational Materials
AU - Pak, Alexander Ya
AU - Sotskov, Vadim
AU - Gumovskaya, Arina A
AU - Vassilyeva, Yuliya Z
AU - Bolatova, Zhanar S
AU - Kvashnina, Yulia A
AU - Mamontov, Gennady Ya.
AU - Shapeev, Alexander V
AU - Kvashnin, Alexander G.
PY - 2023
DA - 2023/01/13 00:00:00
PB - Springer Nature
IS - 1
VL - 9
SN - 2057-3960
ER -
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@article{2023_Pak
author = {Alexander Ya Pak and Vadim Sotskov and Arina A Gumovskaya and Yuliya Z Vassilyeva and Zhanar S Bolatova and Yulia A Kvashnina and Gennady Ya. Mamontov and Alexander V Shapeev and Alexander G. Kvashnin},
title = {Machine learning-driven synthesis of TiZrNbHfTaC5 high-entropy carbide},
journal = {npj Computational Materials},
year = {2023},
volume = {9},
publisher = {Springer Nature},
month = {jan},
url = {https://doi.org/10.1038/s41524-022-00955-9},
number = {1},
doi = {10.1038/s41524-022-00955-9}
}
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Pak, Alexander Ya., et al. “Machine Learning-Driven Synthesis of TiZrNbHfTaC5 High-Entropy Carbide.” Npj Computational Materials, vol. 9, no. 1, Jan. 2023. Crossref, https://doi.org/10.1038/s41524-022-00955-9.