Oxygen Reduction Reaction Activity of Fe-based Dual-Atom Catalysts with Different Local Configurations via Graph Neural Representation

作     者：Xueqian Xia Zengying Ma Yucheng Huang 夏雪倩;马增英;黄玉成

作者机构：College of Chemistry and Material ScienceKey Laboratory of Functional Molecular SolidsMinistry of EducationAnhui Key Laboratory of Molecule-Based MaterialsAnhui Carbon Neutrality Engineering CenterAnhui Normal UniversityWuhu 241000China 

出 版 物：《Chinese Journal of Chemical Physics》 (化学物理学报（英文）)

年 卷 期：2024年第37卷第5期

页      面：599-604,I0038-I0040,I0099页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 070304[理学-物理化学(含∶化学物理)] 08[工学] 0817[工学-化学工程与技术] 0703[理学-化学] 

基　　金：This work was supported by the National Natural Science Foundation of China(No.22473001) the Natural Science Funds for Distinguished Young Scholar of Anhui Province(1908085J08) the University An-nual Scientific Research Plan of Anhui Province(2022AH010013) 

主　　题：Oxygen reduction reaction Dual-atom catalyst Graph neural representation Density functional theory Artificial intelligence 

摘      要：The performance of proton exchange membrane fuel cells depends heavily on the oxygen reduction reaction(ORR)at the cathode,for which platinum-based catalysts are currently the *** high cost and limited availability of platinum have driven the search for alternative *** FeN4 single-atom catalysts have shown promising potential,their ORR activity needs to be further *** contrast,dual-atom catalysts(DACs)offer not only higher metal loading but also the ability to break the ORR scaling ***,the diverse local structures and tunable coordination environments of DACs create a vast chemical space,making large-scale computational screening *** this study,we developed a graph neural network(GNN)-based framework to predict the ORR activity of Fe-based DACs,effectively addressing the challenges posed by variations in local catalyst *** model,trained on a dataset of 180 catalysts,accurately predicted the Gibbs free energy of ORR intermediates and overpotentials,and identified 32 DACs with superior catalytic activity compared to FeN4 *** approach not only advances the design of high-performance DACs,but also offers a powerful computational tool that can significantly reduce the time and cost of catalyst development,thereby accelerating the commercialization of fuel cell technologies.