Zeitschriftenartikel:
J. Timmermann, F. Kraushofer, N. Resch, P. Li, Y. Wang, Z. Mao, M. Riva, Y. Lee, C. Staacke, M. Schmid, C. Scheurer, G. Parkinson, U. Diebold, K. Reuter:
"IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations";
Physical Review Letters,
125
(2020),
S. 2061011
- 2061016.
Kurzfassung englisch:
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.
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1103/PhysRevLett.125.206101
Elektronische Version der Publikation:
https://doi.org/10.1103/PhysRevLett.125.206101
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.