材料科学技术(英文版)2021,Vol.72Issue(13) :8-15.

Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential

Fu-Zhi Dai Yinjie Sun Bo Wen Huimin Xiang Yanchun Zhou
材料科学技术(英文版)2021,Vol.72Issue(13) :8-15.

Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential

Fu-Zhi Dai 1Yinjie Sun 1Bo Wen 1Huimin Xiang 1Yanchun Zhou1
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作者信息

  • 1. Science and Technology on Advanced Functional Composite Laboratory, Aerospace Research Institute of Materials & Processing Technology, Beijing,100076, China
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Abstract

High entropy diborides are new categories of ultra-high temperature ceramics,which are believed promising candidates for applications in hypersonic vehicles.However,knowledge on high temperature thermal and mechanical properties of high entropy diborides is still lacking unit now.In this work,variations of thermal and elastic properties of high entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 with respect to temperature were predicted by molecular dynamics simulations.Firstly,a deep learning potential for Ti-Zr-Hf-Nb-Ta-B diboride system was fitted with its prediction error in energy and force respectively being 9.2 meV/atom and 208 meV/(A),in comparison with first-principles calculations.Then,temperature dependent lattice constants,anisotropic thermal expansions,anisotropic phonon thermal conductivities,and elastic properties of high entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 from 0 ℃ to 2400 ℃ were evaluated,where the predicted room temperature values agree well with experimental measurements.In addition,intrinsic lattice distortions of (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 were analyzed by displacements of atoms from their ideal positions,which are in an order of 10-3 (A) and one order of magnitude smaller than those in (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C.It indicates that lattice distortions in (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 is not so severe as expected.With the new paradigm of machine learning potential,deep insight into high entropy materials can be achieved in the future,since the chemical and structural complexly in high entropy materials can be well handled by machine learning potential.

Key words

High entropy diborides/Machine learning potential/Thermal properties/Elastic properties/Molecular dynamics

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基金项目

Natural Sciences Foundation of China(51972089)

Natural Sciences Foundation of China(51672064)

出版年

2021
材料科学技术(英文版)
中国金属学会 中国材料研究学会 中国科学院金属研究所

材料科学技术(英文版)

CSTPCDCSCDSCI
影响因子:0.657
ISSN:1005-0302
被引量6
参考文献量1
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