Facet-Dependent Oxygen Evolution on IrO2 from Machine-Learned Potential Driven Atomistic Models

Abstract

Hydrogen production by acidic water electrolysis is limited by the anodic oxygen evolution reaction (OER), for which IrO2 remains the state-of-the-art catalyst as it combines low overpotential with comparatively high activity. Yet, the performance of the catalysts is strongly facet dependent, and the atomistic origin of the activity gap across different surface orientations remains unresolved because of discrete interfacial solvation, redox chemistry, and adsorbate energetics coupled under electrochemical conditions. Here, we present a unified thermodynamics and electronic structure analysis of OER on IrO2(110) and IrO2(101) surfaces that explicitly resolves the trends of facet dependent OER activity. We first employed CHE based free energy profiles with implicit solvation, however, the facet-dependent OER trend was not clearly captured. Subsequently, we incorporate explicit water solvation using machine-learned molecular dynamics simulations (MLMD) to obtain statistically equilibrated IrO2/H2O structures. Using water ligated Ir sites as representative catalytic sites, we compute adsorption free energies for OER intermediates and construct free energy diagrams. Explicit solvation dependent reaction pathway identifies *O to *OOH transition as the potential determining step on both facets, with IrO2(110) requiring a substantially lower overpotential than IrO2(101). Electronic structure analyses, including work function, spin-resolved projected density of states, and orbital resolved COHP reveal that the (101) facet stabilizes the Ir-O state more strongly through facet dependent redistribution of Ir (eg/t2g) hybridization with O(2p) states, consistent with larger charge transfer and stronger Ir-O bonding. This stronger oxo stabilization increases the free energy for O-O bond formation, rationalizing the higher overpotential of IrO2(101). Collectively, these results show that realistic explicit solvation model using machine learning potential unveils the facet dependence of OER activity on IrO2.