Combining first principles and machine learning for rapid assessment response of WO_(3) based gas sensors  

作　　者：Ran Zhang Guo Chen Shasha Gao Lu Chen Yongchao Cheng Xiuquan Gu Yue Wang 

机构地区：[1]School of Medical Information and Engineering,Xuzhou Medical University,Xuzhou 221000,China [2]School of Materials Science and Physics,China University of Mining and Technology,Xuzhou 221116,China [3]School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China

出　　处：《International Journal of Mining Science and Technology》2024年第12期1765-1772,共8页矿业科学技术学报（英文版）

基　　金：funded by National Natural Science Foundation of China(No.52303356);Natural Science Foundation of Jiangsu Province(No.BK20210494);Natural Science Foundation of Jiangsu Province(No.BK20221115);National Key Research and Development Project(No.2000YFC2006601);Scientific Research Foundation for Excellent Talents of Xuzhou Medical University(No.D2019032).

摘　　要：The rapid advancement of gas sensitive properties in metal oxides is crucial for detecting hazardous gases in industrial and coal mining environments.However,the conventional experimental trial and error approach poses significant challenges and resource consumption for the high throughput screening of gas sensitive materials.Consequently,this paper introduced a novel screening approach that integrates first principles with machine learning(ML)to rapidly predict the gas sensitivity of materials.Initially,a comprehensive database of multi-physical parameters was established by modeling various adsorption sites on the surface of WO3,which serves as a representative material.Since density functional theory(DFT)is one of the first principles,DFT calculations were conducted to derive essential multi-physical parameters,including bandgap,density of states(DOS),Fermi level,adsorption energy,and structural modifications resulting from adsorption.The collected data was subsequently utilized to develop a cor-relation model linking the multi-physical parameters to gas sensitive performance using intelligent algo-rithms.The model’s performance was assessed through receiver operating characteristic(ROC)curves,confusion matrices,and other evaluation metrics,ultimately achieving a prediction accuracy of 90%for identifying key features influencing gas adsorption performance.This proposed strategy for predicting the gas sensitive characteristics of materials holds significant potential for application in identifying addi-tional gas sensitive properties across various materials.

关 键 词：Machine learning Density functional theory Rapid assessment Gas sensor 

分 类 号：TB381[一般工业技术—材料科学与工程] TP212[自动化与计算机技术—检测技术与自动化装置]