Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations

Abstract

Catalysis at solid-liquid interfaces underpins many energy technologies, yet ab initio simulations that capture interfacial dynamics remain prohibitively expensive. Here we introduce Open Catalyst 2025 (OC25), the largest dataset for solid-liquid interfaces. To demonstrate OC25-trained models as practical tools for electrocatalysis, we investigate CO dimerization on Cu surfaces, a key step in CO$_2$ electroreduction. Using large cells (>800 atoms) and enhanced sampling up to 7 ns - the largest explicit-solvent CO dimerization study to date - we compute free-energy profiles under varied surface charge, cation identity, and surface facet. We find that dimerization is weakly sensitive to charge and cation identity, with appreciable stabilization only at the most negative charge densities, while extension to stepped Cu(310) reveals a more favorable pathway at modest reducing potentials. Our results demonstrate that OC25-trained models provide a scalable tool for investigating electrocatalytic transformations at solid-liquid interfaces, enabling simulations orders of magnitude beyond ab initio methods.