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Temperature Dependent Thermal and Elastic Properties of high entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2):Molecular Dynamics Simulation by Deep Learning Potential

引用

Journal of Materials Science & Technology 2021年第13期72卷 8-15页

作者： Fu-Zhi Dai Yinjie Sun Bo Wen Huimin Xiang Yanchun Zhou Science and Technology on Advanced Functional Composite Laboratory Aerospace Research Institute of Materials&Processing TechnologyBeijing100076China

high entropy diborides are new categories of ultra-high temperature ceramics,which are believed promising candidates for applications in hypersonic ***,knowledge on high temperature thermal and mechanical properties of high entropy diborides is still lacking unit *** this work,variations of thermal and elastic properties of high entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) with respect to temperature were predicted by molecular dynamics ***,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 ***,temperature dependent lattice constants,anisotropic thermal expansions,anisotropic phonon thermal conductivities,and elastic properties of high entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) from 0℃to 2400℃were evaluated,where the predicted room temperature values agree well with experimental *** addition,intrinsic lattice distortions of(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) 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(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))*** indicates that lattice distortions in(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) is not so severe as *** 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.

关键词： high entropy diborides Machine learning potential Thermal properties Elastic properties Molecular dynamics

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