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您的检索式：作者名="Alexander Ya.Pak"
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| 1 | Machine learning-driven synthesis of TiZrNbHfTaC_(5) high-entropy carbide显示文摘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 TiZrNbHfTaC_(5) 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. | Alexander Ya.Pak Vadim Sotskov Arina A.Gumovskaya Yuliya Z.Vassilyeva Zhanar S.Bolatova Yulia A.Kvashnina Gennady Ya.Mamontov Alexander V.Shapeev Alexander G.Kvashnin | 2023 | npj Computational Materials2023，，1： | 0 |
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