Experimental and Computational Study Toward Identifying Active Sites of Supported SnO _x Nanoparticles for Electrochemical CO ₂ Reduction Using Machine‐Learned Interatomic Potentials

Abstract SnO _x has received great attention as an electrocatalyst for CO ₂ reduction reaction (CO ₂ RR), however; it still suffers from low activity. Moreover, the atomic‐level SnO _x structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, CO ₂ RR performance is enhanced by supporting SnO ₂ nanoparticles on two common supports, vulcan carbon and TiO ₂ . Then, electrolysis of CO ₂ at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C. Furthermore, this study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO ₂ reduction and establish a correlation between SnO _x structures and their CO ₂ RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of ^* H and ^* CO ₂ ⁻ , where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in‐depth insights into the rational design and application of SnO _x ‐based electrocatalysts for CO ₂ RR.