Interpretable Machine Learning-Assisted High-Throughput Screening for Understanding NRR Electrocatalyst Performance Modulation between Active Center and C-N Coordination

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外文摘要：Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high-performance electrochemical catalysts.However,exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people.Herein,to accurately identify the factors that affect the performance of N2 reduction,we apply interpretable machine learning(ML)to analyze high-throughput screening results,which is also suited to other surface reactions in catalysis.To expound on the paradigm,33 promising catalysts are screened from 168 carbon-supported candidates,specifically single-atom catalysts(SACs)supported by a BC3 monolayer(TM@VB/C-Nn=0-3-BC3)via high-throughput screening.Subsequently,the hybrid sampling method and XGBoost model are selected to classify eligible and non-eligible catalysts.Through feature interpretation using Shapley Additive Explanations(SHAP)analysis,two crucial features,that is,the number of valence electrons(Nv)and nitrogen substitution(Nn),are screened out.Combining SHAP analysis and electronic structure calculations,the synergistic effect between an active center with low valence electron numbers and reasonable C-N coordination(a medium fraction of nitrogen substitution)can exhibit high catalytic performance.Finally,six superior catalysts with a limiting potential lower than-0.4 V are predicted.Our workflow offers a rational approach to obtaining key information on catalytic performance from high-throughput screening results to design efficient catalysts that can be applied to other materials and reactions.

外文关键词：

electrochemical nitrogen reductionfeature engineeringhigh-throughput screeningmachine learning

作者：

Jinxin Sun、Anjie Chen、Junming Guan、Ying Han、Yongjun Liu、Xianghong Niu、Maoshuai He、Li Shi、Jinlan Wang、Xiuyun Zhang

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作者单位：

College of Physics Science and Technology,Yangzhou University,Yangzhou 225002,China

State Key Laboratory of Organic Electronics and Information Displays(KLOEID)& Institute of Advanced Materials(IAM),School of Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China

College of Chemistry and Molecular Engineering,Qingdao University of Science and Technology,Qingdao 266042,China

School of Physics & Key Laboratory of Quantum Materials and Devices,Ministry of Education,Southeast University,Nanjing 20089,China

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基金：

National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Research Startup Foundation of Recruiting Talents of Nanjing University of Posts and TelecommunicationsSix Talent Peaks Project in Jiangsu Provinceopen research fund of Key Laboratory of Quantum Materials and Devices(Southeast University)

项目编号：

2022YFA1503103220330029226111222203046NY221128XCL-104

出版年：

2024

DOI：

10.1002/eem2.12693

能源与环境材料(英文)

ISSN：

年,卷(期)：2024.7(5)