文献类型：期刊文献

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

作者：Zhou, Lei[1,2,3];Tian, Pengfei[1,2,3];Zhang, Bowei[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

年份：2024

卷号：17

期号：4

起止页码：3352

外文期刊名：NANO RESEARCH

收录：;EI(收录号：20234414982006);WOS:【SCI-EXPANDED(收录号:WOS:001093461100001)】；

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

语种：英文

外文关键词：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 C1N3 coordination environment. Moreover, Y atoms in C-4 coordination environment and Cd atoms in C2N2-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.