Ab initio scale-bridging modelling and design of organic permanent magnets
Abstract
Magnetic materials have many uses, e.g. for electricity production, in the transport sector, for communications and information technologies. Search for new high-performance magnets is, for that reason, strongly motivated and presents a large on-going field of research. However, best known magnets rely on rare-earth elements, associated with negative environmental impact, high costs and complicated energy-intensive synthesis. On the other hand, also organic magnets are known since many years but have not found real-life applications as permanent magnets because of their low performance. Recent progress in that field has shown, however, that it is possible to increase the thermal stability of magnetism and coercive field in such systems, motivating renewed interest in that research field.
Aiming to address this challenge, the proposed project will focus on discovery of new organic magnets with better performance, which would make them suitable for real-life applications, as well as fundamental understanding of the physical mechanism responsible for magnetism in these systems. The latter is profoundly different from inorganic magnets, because magnetism in organic systems is often delocalized over large groups of atoms.
Scale-bridging approach will be used for modelling organic magnets on different length scales, starting with a few Angstrom and proceeding to several hundred nanometers, based on the first principles of quantum mechanics. In step 1, state-of-the-art electronic structure methods will address the electronic properties which correspond to the shortest length scale. The electronic structure will be calculated using density functional theory. In step 2, the magnetic interactions of Heisenberg and Dzyaloshinskii-Moriya types will be calculated based on the electronic properties obtained in step 1 and will provide the necessary information for large-scale modelling of magnetic textures. Finally, atomistic spin dynamics simulations based on the calculated magnetic interactions will address temperature-dependent magnetic properties, such as ordering temperature and magnetic hysteresis.
Structural variety of organic magnets will be explored by considering mechanical pressure and chemical substitution effects in the calculations for known magnets. Machine-learning analysis will be applied to the obtained numerical results, in order to reveal important factors for magnetic performance and to optimize the system structure.
