文献类型：期刊文献

英文题名：Bridging Theory and Experiment: Machine Learning Potential-Driven Insights into pH-Dependent CO2 Reduction on Sn-Based Catalysts

作者：Wang, Yuhang[1,2];Wu, Zelin[3];Jiang, Yingfang[4];Zhang, Di[1];Wang, Qiang[3];Wang, Congwei[3];Li, Huihui[4];Jia, Xue[1];Fan, Jun[2,5];Li, Hao[1]

机构：[1]Tohoku Univ, Adv Inst Mat Res WPI AIMR, Sendai 9808577, Japan;[2]City Univ Hong Kong, Dept Mat Sci & Engn, Hong Kong 999077, Peoples R China;[3]Chinese Acad Sci, Inst Coal Chem, State Key Lab Coal Convers, Shanxi Key Lab Carbon Mat, Taiyuan 030001, Shanxi, Peoples R China;[4]East China Univ Sci & Technol, Sch Chem Engn, Minist Educ, Key Lab Ultrafine Mat, Shanghai 200237, Peoples R China;[5]City Univ Hong Kong, Ctr Adv Nucl Safety & Sustainable Dev, Hong Kong 999077, Peoples R China

年份：2025

卷号：35

期号：36

外文期刊名：ADVANCED FUNCTIONAL MATERIALS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001517901500001)】；

基金：Y.W. and Z.W. contributed equally to this work. This work was supported by Science and Technology Cooperation and Communication Project of Shanxi Province (No. 202304041101016), the Foundation of State Key Laboratory of Coal Conversion (No. J24-25-610), the National Natural Science Foundation of China (52471215 and 22279156), the Natural Science Foundation of Shanxi Province (No. 202103021224440), Shanxi Scholarship Council of China (No. 2024-160), JSPS KAKENHI (Nos. JP25K01737, JP25H01508, JP24K23068, and JP25K17991), Hong Kong Research Grant Council Collaborative Research Fund (Nos. C1002-21G and C1017-22G). The authors acknowledge the Center for Computational Materials Science, Institute for Materials Research, Tohoku University for the use of MASAMUNE-IMR (Nos. 202412-SCKXX-0211 and 202412-SCKXX-0209) and the Institute for Solid State Physics (ISSP) at the University of Tokyo for the use of their supercomputers.

语种：英文

外文关键词：catalysis theory; CO2 reduction reaction; formic acid; machine learning potential; pH-dependent microkinetic modeling

摘要：Sn-based materials are among the most promising catalysts for CO2 reduction reaction (CO2RR) to formic acid. However, the complex electrochemistry-induced surface reconstruction under negative potentials has hindered the precise elucidation of the structure-performance relationship. Herein, machine learning potential (MLP) is employed to accelerate molecular dynamics (MD) simulations, and pH-field coupled microkinetic modelling is perfromed to unravel the pH dependence of CO2RR at the reversible hydrogen electrode (RHE) scale. Encouragingly, the developed MLP reveals that SnO2 adopts a nanorod-like morphology, accurately reproducing experimentally observed reconstruction phenomena. Additionally, SnS2 prefers to form a rougher surface. Leveraging the precisely determined reconstructed surface, the exciting pH-dependent behavior of Sn-based catalysts is highlighted: the increase of pH will cause a left-shift in the CO2RR volcano and ultimately enhance the catalyst's activity. Most importantly, the excellent agreement between the theoretical simulations and our subsequent experimental measurements validates the accuracy of the simulations in terms of turnover frequencies, providing a clear benchmarking analysis between experiments and the MLP-MD-assisted pH-field coupled microkinetic modelling. This work not only offers a valuable MLP-based approach for studying surface reconstructions, but also provides new guidance for the design of high-performance complex catalysts for CO2RR.