文献类型：期刊文献

中文题名：Data-driven rational design of single-atom materials for hydrogen evolution and sensing

作者：Lei Zhou[1,2,3];Pengfei Tian[1,2,3];Bowei Zhang[1,2,3];Fu-Zhen Xuan[1,2,3]

机构：[1]Shanghai Key Laboratory of Intelligent Sensing and Detection Technology,East China University of Science and Technology,Shanghai 200237,China;[2]School of Mechanical and Power Engineering,East China University of Science and Technology,Shanghai 200237,China;[3]Key Laboratory of Pressure Systems and Safety of Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

年份：2024

卷号：17

期号：4

起止页码：3352

中文期刊名：Nano Research

外文期刊名：纳米研究（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2023_2024】；PubMed;

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.

语种：英文

中文关键词：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.