The JuDiT database of impurities embedded into a Topological Insulator

DOI

We present JuDiT (Jülich Database of impurities embedded into a Topological insulator) which collects first principles calculation of impurities embedded into the prototypical topological insulator Sb2Te3. The density functional calculations of this work were performed with the JuKKR package [1], which allows to embed translational invariance breaking impurities into crystalline host system based on the Korringa-Kohn-Rostoker Green function method, and were performed with the AiiDA-KKR package [2]. Our database collects, among others, predicted impurity properties like charge doping introduced by the defects, magnetic moments of the impurities and impurity density of states calculations. We include calculations for the intrinsic Fermi level in the middle of the bulk band gap as well as for shifted Fermi level into valence and conduction band which models different experimental conditions. The impurities were embedded into different layers throughout a 6 quintuple layer thick film which allows to investigate the effect of the topological surface state (localized at the surface) on impurity properties. The JuDiT database allows to uncover chemical trends in impurity properties and can help optimizing the next generation of topological materials.

[1] https://jukkr.fz-juelich.de and www.judft.de [2] https://github.com/JuDFTteam/aiida-kkr

Identifier
DOI https://doi.org/10.24435/materialscloud:2020.0030/v1
Source https://archive.materialscloud.org/record/2020.0030/v1
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:349
Provenance
Creator Rüßmann, Philipp; Bertoldo, Fabian; Blügel, Stefan
Publisher Materials Cloud
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering