文献类型：期刊文献

英文题名：Bridging Theory and Experiment: Machine Learning Potential-Driven Insights into pH-Dependent CO? 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] Advanced Institute for Materials Research [WPI-AIMR], Tohoku University, Sendai, 980-8577, Japan; [2] Department of Materials Science and Engineering, City University of Hong Kong, 999077, Hong Kong; [3] State Key Laboratory of Coal Conversion, Shanxi Key Laboratory of Carbon Materials, Institute of Coal Chemistry, Chinese Academy of Sciences, Shanxi, Taiyuan, 030001, China; [4] Key Laboratory for Ultrafine Materials of Ministry of Education, School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, China; [5] Center for Advance Nuclear Safety and Sustainable Development, City University of Hong Kong, 999077, Hong Kong

年份：2025

卷号：35

期号：36

外文期刊名：Advanced Functional Materials

收录：EI(收录号：20252718704287)

语种：英文

外文关键词：Carbon dioxide - Catalysis - Catalyst activity - Electrodes - Functional materials - IV-VI semiconductors - Learning systems - Machine learning - Molecular dynamics - pH - Reaction kinetics - Reduction - Semiconducting tin compounds - Sulfur compounds - Surface reconstruction

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. ? 2025 The Author(s). Advanced Functional Materials published by Wiley-VCH GmbH.