Theoretical prediction on thermal and mechanical properties of high entropy(Zr(0.2)Hf(0.2)Ti(0.2)Nb(0.2)Ta(0.2))C by deep learning potential

作     者：Fu-Zhi Dai Bo Wen Yinjie Sun Huimin Xiang Yanchun Zhou Fu-Zhi Dai;Bo Wen;Yinjie Sun;Huimin Xiang;Yanchun Zhou

作者机构：Science and Technology on Advanced Functional Composite LaboratoryAerospace Research Institute of Materials&Processing TechnologyBeijing100076China 

出 版 物：《Journal of Materials Science & Technology》 (材料科学技术（英文版）)

年 卷 期：2020年第36卷第8期

页      面：168-174页

核心收录：

学科分类：08[工学] 0817[工学-化学工程与技术] 0806[工学-冶金工程] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0703[理学-化学] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：supported financially by the National Natural Science Foundation of China(Nos.51672064 and No.U1435206)。 

主　　题：High entropy ceramics Machine learning potential Thermal properties Mechanical properties Molecular dynamics Simulation 

摘      要：High entropy materials(HEMs, e.g. high entropy alloys, high entropy ceramics) have gained increasing interests due to the possibility that they can provide challenge properties unattainable by traditional materials. Though a large number of HEMs have emerged, there is still in lack of theoretical predictions and simulations on HEMs, which is probably caused by the chemical complexity of HEMs. In this work,we demonstrate that the machine learning potentials developed in recent years can overcome the complexity of HEMs, and serve as powerful theoretical tools to simulate HEMs. A deep learning potential(DLP) for high entropy(Zr(0.2) Hf(0.2) Ti(0.2) Nb(0.2) Ta(0.2))C is fitted with the prediction error in energy and force being 9.4 me V/atom and 217 me V/?, respectively. The reliability and generality of the DLP are affirmed,since it can accurately predict lattice parameters and elastic constants of mono-phase carbides TMC(TM = Ti, Zr, Hf, Nb and Ta). Lattice constants(increase from 4.5707 ? to 4.6727 ?), thermal expansion coefficients(increase from 7.85×10-6 K^(-1) to 10.58×10-6 K^(-1)), phonon thermal conductivities(decrease from 2.02 W·m-1·K^(-1) to 0.95 W·m-1·K^(-1)), and elastic properties of high entropy(Zr(0.2) Hf(0.2) Ti(0.2) Nb(0.2) Ta(0.2))C in temperature ranging from 0°C to 2400°C are predicted by molecular dynamics simulations. The predicted room temperature properties agree well with experimental measurements, indicating the high accuracy of the DLP. With introducing of machine learning potentials, many problems that are intractable by traditional methods can be handled now. It is hopeful that deep insight into HEMs can be obtained in the future by such powerful methods.