IrO2 Surface Complexions Identified through Machine Learning and Surface 
Investigations

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

doi:10.1103/PhysRevLett.125.206101

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IrO₂ Surface Complexions Identified through Machine Learning and Surface Investigations

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Reuter, Karsten
Theory, Fritz Haber Institute, Max Planck Society;

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Citation

Timmermann, J., Kraushofer, F., Resch, N., Li, P., Wang, Y., Mao, Z., et al. (2020). IrO₂ Surface Complexions Identified through Machine Learning and Surface Investigations. Physical Review Letters, 125(20): 206101. doi:10.1103/PhysRevLett.125.206101.

Cite as: https://hdl.handle.net/21.11116/0000-0007-75BF-F

Abstract

A Gaussian approximation potential was trained using density-functional theory data to enable a globalgeometry optimization of low-index rutile IrO₂ facets through simulated annealing.Ab initiothermo-dynamics identifies (101) and (111) (1×1) terminations competitive with (110) in reducing environments.Experiments on single crystals find that (101) facets dominate and exhibit the theoretically predicted(1×1) periodicity and x-ray photoelectron spectroscopy core-level shifts. The obtained structures areanalogous to the complexions discussed in the context of ceramic battery materials.