(523f) Insights into IrO2 degradation Mechanisms during Oxygen Evolution Reaction Via Machine-Learning Assisted Framework

Iridium (Ir)-based materials are benchmark catalysts for oxygen evolution reaction (OER) in acidic media, owing to their outstanding stability and catalytic activity under operating conditions. Despite their critical role, the mechanisms driving the degradation of Ir-based electrocatalysts remain poorly understood. While extensive experimental and theoretical efforts have advanced our knowledge of OER mechanisms and their influence on catalytic activity, detailed studies on corrosion and degradation pathways are still limited due to the complexity of interfacial processes and the need for extensive sampling. To address these challenges, we developed machine-learning potentials (MLPs) tailored for low-index rutile IrO₂ surfaces. Using these models, we reveal that: i) dissolution mechanisms and energy barriers are strongly dependent on crystallographic orientation, similar to OER activity; ii) chemistry of dissolution intermediates is facet- and potential- dependent; iii) reoxidation kinetics play a critical role in stabilizing the materials under reaction conditions. Our findings offer new atomic-level insights into the stability of IrO₂ catalysts and provide a framework for designing more durable OER materials.

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.