Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

作     者：Jin Li Naiteng Wu Jian Zhang Hong‑Hui Wu Kunming Pan Yingxue Wang Guilong Liu Xianming Liu Zhenpeng Yao Qiaobao Zhang Jin Li;Naiteng Wu;Jian Zhang;Hong-Hui Wu;Kunming Pan;Yingxue Wang;Guilong Liu;Xianming Liu;Zhenpeng Yao;Qiaobao Zhang

作者机构：College of Chemistry and Chemical Engineeringand Henan Key Laboratory of Function‑Oriented Porous MaterialsLuoyang Normal UniversityLuoyang 471934People’s Republic of China New Energy Technology Engineering Lab of Jiangsu ProvinceCollege of ScienceNanjing University of Posts and Telecommunications(NUPT)Nanjing 210023People’s Republic of China School of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijing 100083People’s Republic of China Department of ChemistryUniversity of Nebraska-LincolnLincolnNE 8588USA Henan Key Laboratory of High‑Temperature Structural and Functional MaterialsNational Joint Engineering Research Center for Abrasion Control and Molding of Metal MaterialsHenan University of Science and TechnologyLuoyang 471003People’s Republic of China National Engineering Laboratory for Risk Perception and PreventionBeijing 100041People’s Republic of China Center of Hydrogen ScienceShanghai Jiao Tong UniversityShanghai 200000People’s Republic of China State Key Laboratory of Metal Matrix CompositesSchool of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200000People’s Republic of China State Key Laboratory of Physical Chemistry of Solid SurfacesCollege of MaterialsXiamen UniversityXiamen 361005People’s Republic of China 

出 版 物：《Nano-Micro Letters》 (纳微快报（英文版）)

年 卷 期：2023年第15卷第12期

页      面：161-187页

核心收录：

学科分类：081702[工学-化学工艺] 08[工学] 0817[工学-化学工程与技术] 081104[工学-模式识别与智能系统] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0703[理学-化学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Natural Science Foundation of China(Grant No.22008098,52122408) the Program for Science&Technology Innovation Talents in Universities of Henan Province(No.22HASTIT008) the Programs for Science and Technology Development of Henan Province,China(No.222102320065) the Key Specialized Research and Development Breakthrough(Science and Technology)in Henan Province(No.212102210214) the Natural Science Foundations of Henan Province(No.222300420502) the Key Scientific Research Projects of University in Henan Province(No.23B430002). 

主　　题：Machine learning Hydrogen evolution reaction Low-dimensional electrocatalyst Descriptor Algorithm 

摘      要：Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.Nevertheless,the conventionaltrial and errormethod for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive.Fortunately,the advancement of machine learning brings new opportunities for electrocatalysts discovery and design.By analyzing experimental and theoretical data,machine learning can effectively predict their hydrogen evolution reaction(HER)performance.This review summarizes recent developments in machine learning for low-dimensional electrocatalysts,including zero-dimension nanoparticles and nanoclusters,one-dimensional nanotubes and nanowires,two-dimensional nanosheets,as well as other electrocatalysts.In particular,the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted.Finally,the future directions and perspectives for machine learning in electrocatalysis are discussed,emphasizing the potential for machine learning to accelerate electrocatalyst discovery,optimize their performance,and provide new insights into electrocatalytic mechanisms.Overall,this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.