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

Research output: Contribution to journalArticlepeer-review

Abstract

A Gaussian approximation potential 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) (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 are analogous to the complexions discussed in the context of ceramic battery materials.

Original languageEnglish (US)
Article number206101
JournalPhysical review letters
Volume125
Issue number20
DOIs
StatePublished - Nov 10 2020

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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