Machine learning driven high-throughput screening of S and Ncoordinated SACs for eNRR  

作　　者：Lintao Xu Yuhong Huang Haiping Lin Xiumei Wei Fei Ma 

机构地区：[1]School of Physics and Information Technology,Shaanxi Normal University,Xi’an 710119,China [2]Shaanxi“Four Bodies and One Union”University-Enterprise Joint Research Center for Advanced Molybdenum-based Functional Materials,Xi’an 710119,China [3]State Key Laboratory for Mechanical Behavior of Materials,Xi’an Jiaotong University,Xi’an 710049,China

出　　处：《Nano Research》2025年第4期633-644,共12页纳米研究(英文版)

基　　金：supported by National Natural Science Foundation of China(Nos.52271136 and 22373063);the Natural Science Foundation of Shaanxi Province in China(Nos.2021JC-06 and 2019TD-020);Fundamental Research Funds for the Central Universities of China(No.GK202203002).

摘　　要：This study constructs 196 transition metals(TM)@S_(x)N_(y) single-atom catalysts(SACs)(x=0-4 and y=0-4)and employs the eXtreme Gradient Boosting(XGBoost)classification model in machine learning(ML)for effectively distinguishing qualified and unqualified catalysts.The prediction accuracy rate is high,up to 95%.The SHapley Additive exPlanations(SHAP)analysis reveals that the N≡N bond length and the number of outermost d electrons(N_(d))can well describe the nitrogen(N2)reduction reaction(NRR)activity.The relationships between N≡N,N_(d),the adsorption energies of different intermediates(ΔE_(*N_(2)),ΔE_(*N_(2)H),and ΔE_(*NH_(2))),the general descriptor(φ),and the Gibbs free energy of key steps(ΔG_(*N_(2)),ΔG_(*N_(2)-*N_(2)H),and ΔG_(*N_H(2)-*NH_(3)))indicate that moderate nitrogen activation can enhance the reaction activity.Among the 17 screened SACs,Mo@S3N1,and W@S_(3)N_(1) demonstrate the best catalytic performance,with limiting potential(U_(L))values of only-0.26 and-0.25 V under implicit solvation conditions.The electronic properties and variations in N≡N and TM-N bond lengths are investigated to reveal the origin of NRR activity.This study provides the decisive features and NRR dataset for ML research,as well as a feasible strategy for rational design of NRR SACs.

关 键 词：nitrogen reduction reaction(NRR)process machine learning catalytic descriptors SHapley Additive exPlanations(SHAP)analysis 

分 类 号：O643.36[理学—物理化学]