<div class="csl-bib-body">
<div class="csl-entry">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. <i>Physical Review Letters</i>, <i>125</i>(206101). https://doi.org/10.1103/physrevlett.125.206101</div>
</div>
-
dc.identifier.issn
0031-9007
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/141297
-
dc.description.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.
en
dc.language.iso
en
-
dc.relation.ispartof
Physical Review Letters
-
dc.subject
General Physics and Astronomy
-
dc.title
IrO₂ Surface Complexions Identified through Machine Learning and Surface Investigations
