IrO_{2} Surface Complexions Identified through Machine Learning and Surface Investigations.
Phys Rev Lett
; 125(20): 206101, 2020 Nov 13.
Article
em En
| MEDLINE
| ID: mdl-33258623
ABSTRACT
A Gaussian approximation potential was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO_{2} 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.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Phys Rev Lett
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Alemanha