Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning

作     者：Yu Gan Guanjie Wang Jian Zhou Zhimei Sun 

作者机构：School of Materials Science and EngineeringBeihang UniversityBeijing 100191China Center for Integrated Computational Materials EngineeringInternational Research Institute for Multidisciplinary ScienceBeihang UniversityBeijing 100191China 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2021年第7卷第1期

页      面：1609-1618页

核心收录：

学科分类：080903[工学-微电子学与固体电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 07[理学] 070205[理学-凝聚态物理] 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）] 0702[理学-物理学] 

基　　金：This work is supported by the National Natural Science Foundation of China(51872017) the high-performance computing(HPC)resources at Beihang University 

主　　题：performance doping semiconductors 

摘      要：Layered IV-V-VI semiconductors have immense potential for thermoelectric(TE)applications due to their intrinsically ultralow lattice thermal ***,it is extremely difficult to assess their TE performance via experimental trial-and-error ***,we present a machine-learning-based approach to accelerate the discovery of promising thermoelectric candidates in this chalcogenide *** on a dataset generated from high-throughput ab initio calculations,we develop two highly accurateand-efficient neural network models to predict the maximum ZT(ZT_(max))and corresponding doping type,*** top candidate,n-type Pb_(2)Sb_(2)S_(5),is successfully identified,with the ZT_(max) over 1.0 at 650 K,owing to its ultralow thermal conductivity and decent power ***,we find that n-type Te-based compounds exhibit a combination of high Seebeck coefficient and electrical conductivity,thereby leading to better TE performance under electron doping than hole *** p-type TE performance of Se-based semiconductors is superior to n-type,resulting from large Seebeck coefficient induced by high density-ofstates near valence band edges.