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.
@article{arxiv.2009.11569,
title = {IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations},
author = {Jakob Timmermann and Florian Kraushofer and Nikolaus Resch and Peigang Li and Yu Wang and Zhiqiang Mao and Michele Riva and Yonghyuk Lee and Carsten Staacke and Michael Schmid and Christoph Scheurer and Gareth S. Parkinson and Ulrike Diebold and Karsten Reuter},
journal= {arXiv preprint arXiv:2009.11569},
year = {2020}
}