Computational materials design aims at finding and designing new materials for specific purposes. In this project, computational methods based on first-principles theory will be used to build up a database relevant for applications in spintronics. Our initial focus in this regard will be magnetic double perovskites, which represent a highly interesting and versatile class of systems for this purpose. At present, systematic mapping of the properties and stability of these systems is severely lacking. Large swaths of this class of systems remain unexplored due to the very time consuming work needed to experimentally attempt to systematically synthesize and characterize each of the many thousand possible compounds.
Furthermore, simulations of transport phenomena (charge, spin, heat) for spintronics applications will be performed in several different contexts, and their relation to magnetic properties in nanosystems will be investigated. Topological spin textures, polarons and spin-heat interaction are pertinent examples.
The main methods we use are all based on density functional theory (DFT) in one way or another and consist of a mix of center-provided software and in-house developments, in combination with multi-scale methods, such as atomistic spin dynamics. We also employ various model Hamiltonians and dynamic equations such as the discrete nonlinear Schrödinger equation in this work.
Regarding resources, our estimated needs for 2018 amounts to 810 kcore hours per month.