文献类型：期刊文献

英文题名：Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

作者：Zhang, Jiawei[1,2];Hu, Peijun[1,2,3];Wang, Haifeng[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Mat, Res Inst Ind Catalysis, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Ctr Computat Chem, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China;[3]Queens Univ Belfast, Sch Chem & Chem Engn, Belfast BT9 5AG, Antrim, North Ireland

年份：2020

卷号：124

期号：19

起止页码：10483

外文期刊名：JOURNAL OF PHYSICAL CHEMISTRY C

收录：;EI(收录号：20202908955944);WOS:【SCI-EXPANDED(收录号:WOS:000535281300024)】；

基金：This project was supported by the National Key R&D Program of China (2018YFA0208602), NSFC (21873028, 91945302, and 21622305), and National Ten Thousand Talent Program for Young Top-notch Talents in China, Shanghai ShuGuang project (17SG30).

语种：英文

外文关键词：Catalysis - Catalysts - Chemical analysis - Nickel compounds - Machine learning - Chemisorption - Forecasting

摘要：The prediction of chemisorption energy to facilitate the high-throughput screening of active catalysts has been long pursued but remains challenging. In particular, amorphous materials usually exhibit superior activity and have drawn ever-increasing attention in heterogeneous catalysis. However, the insight into the basic structure-property relation remains far from sufficient owing to their disordered structure and untracked surface state, let alone the effective prediction of adsorption energy. Here, employing the amorphous Ni2P catalyst as an example and powerful machine learning (ML) models, we propose an effective strategy that enables fast and quantitative prediction of the adsorption energy of hydrogen on amorphous Ni2P surfaces. Specifically, our method decomposes the difficult prediction of adsorption energy on amorphous surfaces into two subproblems: frozen adsorption energy and relaxation energy. By training with a set of ab initio adsorption energies within a wide configuration space, we succeed to predict the adsorption energies with similar to 0.1 eV error by adopting the feature only relying on local chemical environment. Our strategy allows us to successfully implement the high-throughput exploration of active sites for hydrogen evolution reaction (HER). This work builds a predictive model of site-specific chemisorption energy, and the related statistical analysis underpins the fundamental understanding of the chemical bond, which could largely facilitate rational design of active amorphous catalysts.