Ab-initio methodologies for complex magnetism and magneto-superconductivity
To sustain the growth demand of information and communications technologies beyond the reduction of transistor size (Moore's law), there is an increased need to develop new material-device concepts that achieve high computing performance with low energy investment. Therefore there is a major focus in current solid state research on complex magnetism and magneto-superconductivity. There appears to be two major points of focus. One is to exploit the exotic properties of new composite materials with a complex magnetic ground state, attributed to multi-site or Dzyaloshinskii-Moriya interactions, or altermagnets. The other one is the emerging field of superconducting spintronics which has also garnered significant attention as a promising avenue for developing novel electronic devices that integrate the advantages of superconductivity, such as dissipationless electrical currents, with the manipulation and control of electron spin.
Many of the phenomena related to this field have so far been described only on the level of simplistic models. In our view, a paradigm shift is required in the theoretical description of normal state and superconducting spintronic devices towards Density Functional Theory (DFT)-based methods to allow quantitatively accurate, material-specific predictions of the relevant quantities, that go beyond the quasiclassical approximation and capable of aiding their design. Methods based on Green functions such as the Korringa-Kohn-Rostoker (KKR) and the linearized muffin-tin orbital (LMTO) methods have demonstrated their capability to deliver along these goals many times in the past. Applications to describe noncollinear magnetism and spintronics range from the quantification of magnetic interactions trough the electronic-structure-based description of magnetic skyrmions to various transport phenomena in spintronic heterostructures. Additionally, these methods have already been used to simulate the properties of material systems with coexisting magnetic and superconducting orders by solving the self-consistent Bogoliubov-de Gennes equations in a DFT framework. Furthermore, Green function methods are also just about the ideal tool for the description of impurity atoms and their organized structures in superconductors and on their surfaces. However, the more and more complex problems require novel and ever more complex computational tools, expanding the range of application of these methods, but also using more powerful computers to the level where sustainability also starts to play an important role.
Objectives
The advancement of computational technologies strongly supports the scientific and technological goals highlighted above, however it requires the fast and frequent sharing of ideas, to which we aim to contribute via this workshop. The proposal is, to bring together research groups actively developing methods that allow quantitatively accurate, material-specific predictions of various phenomena in future spintronic devices in the normal and superconducting state.
The plan is to discuss fundamental challenges at the method development level, crucial for the Psi-k community, and promote the exchange of ideas and stimulating new research directions with a focus on:
· Code and method developments: GPU based programming KKR-GF, sustainable code developments for exascale computing
· Applications of machine learning strategies and high-throughput calculations
· Superconducting multilayers, Josephson junctions, and impurity systems (including transport)
· Exotic spin configurations (skyrmions, hopfions, new types of domain walls; including the role of DMI and multispin and multipole interactions)
· Altermagnets: basics and potential applications
· Magneto-transport properties and dynamics in the presence of superconductivity.
Our goal is also to give young researchers more visibility (e.g., by providing slots as invited speakers) and to foster networking opportunities, while benefiting from the support and expertise of well-established scientists.
