Open Access
npj Computational Materials, volume 9, issue 1, publication number 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
,
Publication type: Journal Article
Publication date: 2023-01-13
Journal:
npj Computational Materials
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 9.7
ISSN: 20573960
Computer Science Applications
General Materials Science
Mechanics of Materials
Modeling and Simulation
Abstract
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.
Citations by journals
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1
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Dalton Transactions
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Dalton Transactions
1 publication, 14.29%
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Computational Materials Science
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Computational Materials Science
1 publication, 14.29%
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Coatings
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Coatings
1 publication, 14.29%
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Russian Physics Journal
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Russian Physics Journal
1 publication, 14.29%
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New Journal of Chemistry
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New Journal of Chemistry
1 publication, 14.29%
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Journal of the European Ceramic Society
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Journal of the European Ceramic Society
1 publication, 14.29%
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International Journal of Refractory Metals and Hard Materials
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International Journal of Refractory Metals and Hard Materials
1 publication, 14.29%
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1
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Citations by publishers
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1
2
3
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Elsevier
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Elsevier
3 publications, 42.86%
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Royal Society of Chemistry (RSC)
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Royal Society of Chemistry (RSC)
2 publications, 28.57%
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 14.29%
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Springer Nature
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Springer Nature
1 publication, 14.29%
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1
2
3
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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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%2Fs41524-022-00955-9
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%2Fs41524-022-00955-9},
number = {1},
doi = {10.1038/s41524-022-00955-9}
}