Neural Network Accelerated Investigation of the Dynamic Structure–Performance Relations of Electrochemical CO2 Reduction over SnOx Surfaces
Lulu Li,
Zhi-Jian Zhao,
Gong Zhang,
Dongfang Cheng,
Xin Chang,
Xintong Yuan,
Tuo Wang,
Jinlong Gong
Affiliations
Lulu Li
School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education,
Tianjin University, Tianjin 300072, China.
Zhi-Jian Zhao
School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education,
Tianjin University, Tianjin 300072, China.
Gong Zhang
School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education,
Tianjin University, Tianjin 300072, China.
Dongfang Cheng
School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education,
Tianjin University, Tianjin 300072, China.
Xin Chang
School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education,
Tianjin University, Tianjin 300072, China.
Xintong Yuan
School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education,
Tianjin University, Tianjin 300072, China.
Tuo Wang
School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education,
Tianjin University, Tianjin 300072, China.
Jinlong Gong
School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education,
Tianjin University, Tianjin 300072, China.
Heterogeneous catalysts, especially metal oxides, play a curial role in improving energy conversion efficiency and production of valuable chemicals. However, the surface structure at the atomic level and the nature of active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. This paper describes a strategy of the multiscale simulation to investigate the SnOx reduction process and to build a structure–performance relation of SnOx for CO2 electroreduction. Employing high-dimensional neural network potential accelerated molecular dynamics and stochastic surface walking global optimization, coupled with density functional theory calculations, we propose that SnO2 reduction is accompanied by surface reconstruction and charge density redistribution of active sites. A regulatory factor, the net charge, is identified to predict the adsorption capability for key intermediates on active sites. Systematic electronic analyses reveal the origin of the interaction between the adsorbates and the active sites. These findings uncover the quantitative correlation between electronic structure properties and the catalytic performance of SnOx so that Sn sites with moderate charge could achieve the optimally catalytic performance of the CO2 electroreduction to formate.