Robust combined modeling of crystalline and amorphous silicon grain boundary conductance by machine learning

作     者：Chayaphol Lortaraprasert Junichiro Shiomi 

作者机构：Department of Mechanical EngineeringThe University of TokyoTokyoJapan Institute of Engineering InnovationThe University of TokyoTokyoJapan 

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

年 卷 期：2022年第8卷第1期

页      面：2099-2106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] 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：This work was partially supported by JST-CREST(Grant No.JPMJCR21O2) 

主　　题：grain structure. crystalline 

摘      要：Knowledge in thermal and electric transport through grain boundary(GB)is crucial for designing nanostructured thermoelectric materials,where the transport greatly depends on GB atomistic *** this work,we employ machine learning(ML)techniques to study the relationship between silicon GB structure and its thermal and electric boundary conductance(TBC and EBC)calculated by Green’s function *** present a robust ML prediction model of TBC covering crystalline–crystalline and crystalline–amorphous interfaces,using disorder descriptors and atomic *** also construct high-accuracy ML models for predicting both TBC and EBC and their ratio,using only small data of crystalline *** found that the variations of interatomic angles and distance at GB are the most predictive descriptors for TBC and EBC,*** results demonstrate the robustness of the black-box model and open the way to decouple thermal and electrical conductance,which is a key physical problem with engineering needs.