IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations

TL;DR

This study combines machine learning, surface investigations, and experiments to identify surface complexions on IrO2, revealing dominant facets and their atomic structures relevant for catalytic and battery applications.

Contribution

It introduces a Gaussian Approximation Potential trained on DFT data for global geometry optimization of IrO2 surfaces, linking theoretical predictions with experimental validation.

Findings

(101) facets dominate on single crystals

Theoretical (1x1) periodicity matches experimental XPS data

Structures resemble complexions in ceramic battery materials

Abstract

A Gaussian Approximation Potential (GAP) was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO2 facets through simulated annealing. Ab initio thermodynamics identifies (101) and (111) (1x1)-terminations competitive with (110) in reducing environments. Experiments on single crystals find that (101) facets dominate, and exhibit the theoretically predicted (1x1) periodicity and X-ray photoelectron spectroscopy (XPS) core level shifts. The obtained structures are analogous to the complexions discussed in the context of ceramic battery materials.