Accelerating the Screening of Modified MA_(2)Z_(4) Catalysts for Hydrogen Evolution Reaction by Deep Learning-Based Local Geometric Analysis  

作　　者：Jingnan Zheng Shibin Wang Shengwei Deng Zihao Yao Junhua Hu Jianguo Wang 

机构地区：[1]Institute of Industrial Catalysis,State Key Laboratory Breeding Base of Green-Chemical Synthesis Technology,College of Chemical Engineering,Zhejiang University of Technology,Hangzhou 310032,China [2]School of Materials Science and Engineering,Zhengzhou University,Zhengzhou 450001,China [3]School of Materials Science and Engineering,State Center for International Cooperation on Designer Low-Carbon&Environmental Materials(CDLCEM),Zhengzhou University,Zhengzhou 450001,China

出　　处：《Energy & Environmental Materials》2024年第6期290-302,共13页能源与环境材料（英文）

基　　金：supported by the National Key R&D Program of China(2021YFA1500900);National Natural Science Foundation of China(U21A20298,22141001).

摘　　要：Machine learning(ML)integrated with density functional theory(DFT)calculations have recently been used to accelerate the design and discovery of single-atom catalysts(SACs)by establishing deep structure–activity relationships.The traditional ML models are always difficult to identify the structural differences among the single-atom systems with different modification methods,leading to the limitation of the potential application range.Aiming to the structural properties of several typical two-dimensional MA_(2)Z_(4)-based single-atom systems(bare MA_(2)Z_(4) and metal single-atom doped/supported MA_(2)Z_(4)),an improved crystal graph convolutional neural network(CGCNN)classification model was employed,instead of the traditional machine learning regression model,to address the challenge of incompatibility in the studied systems.The CGCNN model was optimized using crystal graph representation in which the geometric configuration was divided into active layer,surface layer,and bulk layer(ASB-GCNN).Through ML and DFT calculations,five potential single-atom hydrogen evolution reaction(HER)catalysts were screened from chemical space of 600 MA_(2)Z_(4)-based materials,especially V_(1)/HfSn_(2)N_(4)(S)with high stability and activity(Δ_(GH*)is 0.06 eV).Further projected density of states(pDOS)analysis in combination with the wave function analysis of the SAC-H bond revealed that the SAC-dz^(2)orbital coincided with the H-s orbital around the energy level of−2.50 eV,and orbital analysis confirmed the formation ofσbonds.This study provides an efficient multistep screening design framework of metal single-atom catalyst for HER systems with similar two-dimensional supports but different geometric configurations.

关 键 词：graph convolutional neural network hydrogen evolution reaction modified MA_(2)Z_(4) substrate single atom catalyst 

分 类 号：O643.36[理学—物理化学]