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arXiv · 2009.11569

IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations

Jakob Timmermann·Florian Kraushofer·Nikolaus Resch·Peigang Li·Yu Wang·Zhiqiang Mao·Michele Riva·Yonghyuk Lee·Carsten Staacke·Michael Schmid·Christoph Scheurer·Gareth S. Parkinson·Ulrike Diebold·Karsten Reuter
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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.

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Jakob Timmermann, Florian Kraushofer, Nikolaus Resch, Peigang Li, Yu Wang, Zhiqiang Mao, Michele Riva, Yonghyuk Lee, Carsten Staacke, Michael Schmid, Christoph Scheurer, Gareth S. Parkinson, Ulrike Diebold, Karsten Reuter. 2020-09-24. IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations. https://doi.org/10.1103/physrevlett.125.206101

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