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

ABSTRACT: 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 SnO _x reduction process and to build a structure-performance relation of SnO _x 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 propos

SUBMITTER: Li L 

PROVIDER: S-EPMC10013797 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature