Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
作者机构：Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaILUSA Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbanaILUSA
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：797-808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)] 081704[工学-应用化学] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0817[工学-化学工程与技术] 0835[工学-软件工程] 0703[理学-化学] 070301[理学-无机化学] 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 supported by the National Science Foundation(USA)under grant#1545907 This research is part of the Blue Waters sustained-petascale computing project,which is supported by the National Science Foundation(awards OCI-0725070 and ACI-1238993) the state of Illinois.Assistance from Dr.Debashish Das in collecting the data is gratefully acknowledged
主 题：perovskite conductivity energy
摘 要：We use machine learning tools for the design and discovery of ABO3-type perovskite oxides for various energy applications,using over 7000 data points from the *** demonstrate a robust learning framework for efficient and accurate prediction of total conductivity of perovskites and their classification based on the type of charge carrier at different conditions of temperature and *** evaluating a set of100 features,we identify average ionic radius,minimum electronegativity,minimum atomic mass,minimum formation energy of oxides for all B-site,and B-site dopant ions of the perovskite as the crucial and relevant predictors for determining conductivity and the type of charge *** models are validated by predicting the conductivity of compounds absent in the training *** screen 1793 undoped and 95,832 A-site and B-site doped perovskites to report the perovskites with high conductivities,which can be used for different energy applications,depending on the type of the charge carriers.
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