-water interface from machine learning potentials*

Heterogeneous electrocatalysts for the oxygen evolution reaction (OER) operate via inner-sphere processes that are highly sensitive to the composition and structure of the catalyst’s aqueous interface.  We investigate the proton transfer mechanisms at the aqueous interface of rutile IrO2, one of the most efficient catalytic materials for the OER, using large scale molecular dynamics simulations with ab-initio based machine learning potentials. The intrinsic proton affinities of the different IrO2(110) surface sites are characterized by calculating their acid dissociation constants, which yield a point of zero-charge in good agreement with experiments. A large fraction (≈ 80%) of adsorbed water dissociation is predicted, together with a short lifetime (≈ 0.5 ns) of the resulting terminal hydroxyls, due to rapid proton exchanges between adsorbed H2O and OH species at adjacent surface Ir sites. This rapid surface proton transfer supports the suggestion that the rate-determining step in the OER may not involve proton transfer across the double layer into solution, but rather depend on the concentration of oxidized sites formed by the deprotonation of terminal and bridging hydroxyls, as indicated by recent experiments.