Data-driven rational design of single-atom materials for hydrogen evolution and sensing

作     者：Lei Zhou Pengfei Tian Bowei Zhang Fu-Zhen Xuan Lei Zhou;Pengfei Tian;Bowei Zhang;Fu-Zhen Xuan

作者机构：Shanghai Key Laboratory of Intelligent Sensing and Detection TechnologyEast China University of Science and TechnologyShanghai 200237China School of Mechanical and Power EngineeringEast China University of Science and TechnologyShanghai 200237China Key Laboratory of Pressure Systems and Safety of Ministry of EducationEast China University of Science and TechnologyShanghai 200237China 

出 版 物：《Nano Research》 (纳米研究（英文版）)

年 卷 期：2024年第17卷第4期

页      面：3352-3358页

核心收录：

学科分类：07[理学] 070304[理学-物理化学(含∶化学物理)] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0702[理学-物理学] 

基　　金：supported by the National Natural Science Foundation of China(Nos.52105145 and 12274124) the Shanghai Pilot Program for Basic Research(No.22TQ1400100-6) the Fundamental Research Funds for the Central Universities. 

主　　题：machine learning single-atom materials sensing hydrogen evolution 

摘      要：Herein we proposed a data-driven high-throughput principle to screen high-performance single-atom materials for hydrogen evolution reaction(HER)and hydrogen sensing by combing the theoretical computations and a topology-based multi-scale convolution kernel machine learning algorithm.After the rational training by 25 groups of data and prediction of all 168 groups of single-atom materials for HER and sensing,respectively,a high prediction accuracy(0.931 R^(2) score)was achieved by our model.Results show that the promising HER catalysts include Pt atoms in C_(4) and Sc atoms in C_(1)N_(3) coordination environment.Moreover,Y atoms in C_(4) coordination environment and Cd atoms in C_(2)N_(2)-ortho coordination environment were predicted with great potential as hydrogen sensing materials.This method provides a way to accelerate the discovery of innovative materials by avoiding the time-consuming empirical principles in experiments.