Transfer learning aided high-throughput computational design of oxygen evolution reaction catalysts in acid conditions  

作　　者：Siwen Wang Honghong Lin Yui Wakabayashi Li Qin Zhou Charles A.Roberts Debasish Banerjee Hongfei Jia Chen Ling 

机构地区：[1]Toyota Research Institute of North America,Ann Arbor,MI 48105,USA

出　　处：《Journal of Energy Chemistry》2023年第5期744-757,I0015,共15页能源化学（英文版）

摘　　要：Sluggish oxygen evolution reaction(OER)in acid conditions is one of the bottlenecks that prevent the wide adoption of proton exchange membrane water electrolyzer for green hydrogen production.Despite recent advancements in developing high-performance catalysts for acid OER,the current electrocatalysts still rely on iridium-and ruthenium-based materials,urging continuous efforts to discover better performance catalysts as well as reduce the usage of noble metals.Pyrochlore structured oxide is a family of potential high-performance acid OER catalysts with a flexible compositional space to tune the electrochemical capabilities.However,exploring the large composition space of pyrochlore compounds demands an imperative approach to enable efficient screening.Here we present a highthroughput screening pipeline that integrates density functional theory calculations and a transfer learning approach to predict the critical properties of pyrochlore compounds.The high-throughput screening recommends three sets of candidates for potential acid OER applications,totaling 61 candidates from 6912 pyrochlore compounds.In addition to 3d-transition metals,p-block metals are identified as promising dopants to improve the catalytic activity of pyrochlore oxides.This work demonstrates not only an efficient approach for finding suitable pyrochlores towards acid OER but also suggests the great compositional flexibility of pyrochlore compounds to be considered as a new materials platform for a variety of applications.

关 键 词：PYROCHLORE Acid OER HIGH-THROUGHPUT Machine learning 

分 类 号：TQ116.2[化学工程—无机化工] TQ426