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MM: Fachverband Metall- und Materialphysik
MM 26: Topical Session: Data Driven Materials Science - Machine Learning for Materials Properties
MM 26.5: Vortrag
Dienstag, 17. März 2020, 15:15–15:30, BAR 205
Screening impurity effects in topological insulators with the AiiDA-KKR plugin — •Philipp Rüßmann1, Fabian Bertoldo1,2, Phivos Mavropoulos3, and Stefan Blügel1 — 1Peter Grünberg Institut and Institute for Advanced Simulation, Forschungszentrum Jülich and JARA, D-52425 Jülich, Germany — 2Technical University of Denmark, Kgs. Lyngby, Denmark — 3Physics Department, National and Kapodistrian University of Athens, Greece
The ability to utilize the predictive power of ab initio calculations through automated computing enables scanning of the material space with subsequent materials/properties optimization. We present the AiiDA-KKR plugin [1] which enables high-throughput calculations using the Jülich full-potential relativistic Korringa-Kohn-Rostoker Green function method (KKR) [2] to the AiiDA framework [3]. The KKR method allows, for instance, to calculate the electronic structure of defects embedded into crystalline solids. We applied this scheme to screen the effect of impurities in the strong topological insulator Sb2Te3. Several thousand impurities have been considered, taking into account both the distance of the impurity to the surface as well as the effect of possible changes in the host material’s Fermi level. Our data reveals chemical trends relevant, for example, to transport properties in topological insulators. – We acknowledge the Center of Excellence MaX (EU H2020-INFRAEDI-2018) for financial support.
[1] https://github.com/JuDFTteam/aiida-kkr
[2] https://jukkr.fz-juelich.de
[3] G. Pizzi, et al., Comp. Mat. Sci. 111, 218-230 (2016).