Current and future technology heavily rely on the quantum nature of materials. Examples are found everywhere with applications in information technology, quantum computing, or energy efficient electronics. Superconductivity plays a major role in these materials and its understanding in complex material heterostructures is of great importance.

In our group we develop and apply density functional theory based methods that are capable of describing heterostructure of superconductors and non-superconductors which are based on the Bogoliubov-de Gennes extension to our JuKKR density functional theory code (https://jukkr.fz-juelich.de).

Together with high-throughput automation of our calculations (https://github.com/JuDFTteam/aiida-kkr) and the application of machine learning approaches we seek a deeper understanding of the physics of such complex heterostructures and aim at the computational design of materials for future quantum technologies.

The JuDiT database (https://go.fzj.de/judit) is an example database that demonstrates our high-throughput capabilities for impurities embedded into topological insulators.