文献类型：期刊文献

英文题名：Data-Driven High-Throughput Screening of High-Performance Single-Atom Catalysts for Hydrogen Evolution and Hydrogen Sensing

作者：Zhang, Xiangyu[1,2,3];Zhou, Lei[1,2,3];Chu, Tianshu[1,2,3];Rong, Chao[1,2,3];Cheng, Weiwei[4];Zhu, Jiaqing[4];Zhang, Bowei[1,2,3];Wang, Tao[1,2,3];Xuan, Fu-Zhen[1,2,3]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China;[4]Shanghai Univ Engn Sci, Sch Mat Sci & Engn, Shanghai 201620, Peoples R China

年份：2025

卷号：8

期号：16

起止页码：12083

外文期刊名：ACS APPLIED ENERGY MATERIALS

收录：;EI(收录号：20255119713824);WOS:【SCI-EXPANDED(收录号:WOS:001545729200001)】；

基金：This work was supported by the National Natural Science Foundation of China (No.52422505, No.12274124, No.62301314), the Shanghai Pilot Program for Basic Research (No. 22TQ1400100-6), the Science and Technology Commission of Shanghai Municipality (22ZR1415700), the Shanghai Rising-star Program (20QA1402400), and the Fundamental Research Funds for the Central Universities. Additional support was provided by the Frontiers Science Center for Materiobiology and Dynamic Chemistry and the Feringa Nobel Prize Scientist Joint Research Center at the East China University of Science and Technology.

语种：英文

外文关键词：machine learning; single-atom catalysts; hydrogensensing; hydrogen-evolution reaction; MXenes; graph neural network; density functional theory

摘要：The exploration of high-performance catalytic materials has attracted significant attention due to their substantial economic value. However, the vast material search space and inherent limitations of conventional experimental trial-and-error methods pose significant challenges in exploring these catalytic materials. Herein, we propose a data-driven high-throughput approach for screening high-performance single-atom catalysts (SACs) suitable for hydrogen evolution reactions (HER) and hydrogen sensing applications. This methodology integrates density functional theory (DFT) calculations and a graph neural network (GNN)-based machine learning algorithm. Our results indicate that this data-driven approach effectively predicts SACs for HER and hydrogen sensing applications. This integrated framework significantly accelerates the discovery and development of high-performance catalytic materials, thereby advancing hydrogen-related technologies.