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

Key Points

• This research aims to explore the effectiveness of IrO2 as a catalyst for the oxygen evolution reaction in acidic water electrolysis.
• Utilized machine-learned potential driven atomistic models to analyze the performance of IrO2.
• Focus on the anodic oxygen evolution reaction under acidic conditions.
• Evaluated the intrinsic characteristics of different facets of IrO2.
• Machine learning models indicate specific facets of IrO2 enhance oxygen evolution efficiency.
• Observed low overpotential in specific configurations, outperforming traditional methods.
• Demonstrated potential pathways for improving hydrogen production via water electrolysis.