Thermal conductivity of GeTe crystals based on machine learning potentials

作     者：张健 张昊春 李伟峰 张刚 Jian Zhang;Hao-Chun Zhang;Weifeng Li;Gang Zhang

作者机构：School of Energy Science and EngineeringHarbin Institute of TechnologyHarbin 150001China Institute of High Performance ComputingAgency for ScienceTechnology and ResearchSingapore 138632Singapore School of Physics&State Key Laboratory of Crystal MaterialsShandong UniversityJinan 250100China 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2024年第33卷第4期

页      面：104-107页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project supported by the A*STAR Computational Resource Centre through the use of its high-performance computing facilities financial support from the China Scholarship Council (Grant No.202206120136) 

主　　题：machine learning potentials thermal conductivity molecular dynamics 

摘      要：GeTe has attracted extensive research interest for thermoelectric *** this paper,we first train a neuroevolution potential(NEP)based on a dataset constructed by ab initio molecular dynamics,with the Gaussian approximation potential(GAP)as a *** phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results,with the GAP potential having higher ***,the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials,and both of them are in good agreement with the experimental results;however,the calculation speed when using the NEP potential is about 500 times faster than when using the GAP ***,the lattice thermal conductivity in the range of 300 K-600 K is calculated using the NEP *** lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic *** study provides a theoretical tool for the study of the thermal conductivity of GeTe.