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The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic vehicles. However, the substance that has the highest melting point (Tm) keeps a secret, since precise measurements in extreme conditions are overwhelmingly difficult. In the present work, an accurate deep potential (DP) model of a Hf–Ta–C–N system was first trained, and then applied to search for the highest melting point material by molecular dynamics (MD) simulation and Bayesian global optimization (BGO). The predicted melting points agree well with the experiments and confirm that carbon site vacancies can enhance the melting point of rock-salt-structure carbides. The solid solution with N is verified as another new and more effective melting point enhancing approach for HfC, while a conventional routing of the solid solution with Ta (e.g., HfTa4C5) is not suggested to result in a maximum melting point. The highest melting point (~4236 K) is achieved with the composition of HfC0.638N0.271, which is ~80 K higher than the highest value in a Hf–C binary system. Dominating mechanism of the N addition is believed to be unstable C–N and N–N bonds in liquid phase, which reduces liquid phase entropy and renders the liquid phase less stable. The improved melting point and less gas generation during oxidation by the addition of N provide a new routing to modify thermal protection materials for the hypersonic vehicles.


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The highest melting point material: Searched by Bayesian global optimization with deep potential molecular dynamics

Show Author's information Yinan WangaBo WenbXingjian JiaobYa LicLei Chend,e( )Yujin Wangd,eFu-Zhi Daia,f( )
Artificial Intelligence for Science Institute, Beijing 100084, China
Science and Technology on Advanced Functional Composite Laboratory, Aerospace Research Institute of Materials & Processing Technology, Beijing 100076, China
College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin 150001, China
Institute for Advanced Ceramics, School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Key Laboratory of Advanced Structural–Functional Integration Materials & Green Manufacturing Technology, Harbin Institute of Technology, Harbin 150001, China
DP Technology, Beijing 100080, China

Abstract

The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic vehicles. However, the substance that has the highest melting point (Tm) keeps a secret, since precise measurements in extreme conditions are overwhelmingly difficult. In the present work, an accurate deep potential (DP) model of a Hf–Ta–C–N system was first trained, and then applied to search for the highest melting point material by molecular dynamics (MD) simulation and Bayesian global optimization (BGO). The predicted melting points agree well with the experiments and confirm that carbon site vacancies can enhance the melting point of rock-salt-structure carbides. The solid solution with N is verified as another new and more effective melting point enhancing approach for HfC, while a conventional routing of the solid solution with Ta (e.g., HfTa4C5) is not suggested to result in a maximum melting point. The highest melting point (~4236 K) is achieved with the composition of HfC0.638N0.271, which is ~80 K higher than the highest value in a Hf–C binary system. Dominating mechanism of the N addition is believed to be unstable C–N and N–N bonds in liquid phase, which reduces liquid phase entropy and renders the liquid phase less stable. The improved melting point and less gas generation during oxidation by the addition of N provide a new routing to modify thermal protection materials for the hypersonic vehicles.

Keywords: carbides, carbonitrides, melting point (Tm), deep potential (DP), Bayesian global optimization (BGO)

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Publication history

Received: 16 November 2022
Revised: 30 December 2022
Accepted: 19 January 2023
Published: 13 March 2023
Issue date: April 2023

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© The Author(s) 2023.

Acknowledgements

Dr. Lei Chen would thank for the supports by the National Natural Science Foundation of China (Nos. 52032002, 51972081, and U2130103), University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (No. UNPYSCT-2020052), and Heilongjiang Touyan Team Program. This work was supported by Bohrium Cloud Platform of DP Technology.

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