Record link: 
http://hdl.handle.net/20.500.12708/141297
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Title: 
IrO₂ Surface Complexions Identified through Machine Learning and Surface Investigations
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Citation: 
Timmermann, J., Kraushofer, F., Resch, N., Li, P., Wang, Y., Mao, Z., Riva, M., Lee, Y., Staacke, C., Schmid, M., Scheurer, C., Parkinson, G. S., Diebold, U., & Reuter, K. (2020). IrO₂ Surface Complexions Identified through Machine Learning and Surface Investigations. Physical Review Letters, 125(206101). https://doi.org/10.1103/physrevlett.125.206101
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Publisher DOI: 
10.1103/physrevlett.125.206101
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
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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Organisational Unit: 
E134-05 - Forschungsbereich Surface Physics 
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Journal: 
Physical Review Letters 
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ISSN: 
0031-9007
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Date (published): 
2020
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Number of Pages: 
6
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Peer reviewed: 
Yes
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Keywords: 
General Physics and Astronomy
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Abstract: 
A Gaussian approximation potential was trained using density-functional theory data to enable a globalgeometry optimization of low-index rutile IrO2facets 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.
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Research Areas: 
Materials Characterization: 100%
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Science Branch: 
Physik, Astronomie
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