IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations
| Autor: | 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 |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Phys. Rev. Lett. 125, 206101 (2020) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1103/PhysRevLett.125.206101 |
| Popis: | 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. Comment: 13 pages 2 figures |
| Databáze: | arXiv |
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