Neural Network Accelerated Investigation of the Dynamic Structure- Performance Relations of Electrochemical CO2 Reduction over SnOX Surfaces

Heterogeneous catalysts, especially metal oxides, play a curial role in promoting 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 by the multi-scale simulation to investigate SnOX reduction process and builds up a structure -performance relation of SnOX for CO2 electroreduction (CO2ER). By employing high dimensional neural network (NN) potential accelerated molecular dynamics (MD) and SSW global optimization, coupled with density functional theory (DFT) 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, that Sn sites with moderate charge could achieve the optimally catalytic performance of the CO2ER to formate.