Data-driven materials discovery and computation of nanomagnetic properties from first principles
||Data-driven materials discovery and computation of nanomagnetic properties from first principles|
||SNIC Medium Compute|
||Anna Delin <email@example.com>|
||Kungliga Tekniska högskolan|
||2019-07-01 – 2020-07-01|
||10304 10407 |
The overall aim in the present project is to, via quantum mechanical simulations, perform research that will result in the discovery and development of suitable materials for new types of more environmentally friendly electronics compared to today's technology. Specifically, spintronic nano-sized devices -- i.e. electronic components based on the spin degree of freedom -- are envisaged to be able to run on much less power than conventional electronics and are also relevant for the development of new computational paradigms such as neuromorphic computing. To this aim, we pursue research focussed on method development and systematic and accurate computation of properties of magnetic materials. Our approach will subsequently allow us to use modern data-driven analysis and prediction tools such as machine learning for efficient materials discovery and design. In this project, computations of magnetic materials properties will be performed to build up a database relevant for applications in spintronics. Furthermore, in the context of nano-sized spintronics devices, the finite-temperature properties as well as the transport properties (of spin, charge, and heat) of the spintronic materials are of central importance and therefore form an important part of the database mentioned above. We have for this reason during the last years devoted large efforts to develop computational tools for accurate simulation of finite-temperature and transport properties and we are now in a position to perform production runs of combined molecular and spin dynamics with first principles accuracy.