Data driven magnet design through combinatorial synthesis and micromagnetic graph networks

The reduction of green¬house gas emissions, most importantly CO2, has gained top priority in the worldwide agenda. Electrification of transport and renewable energies heavily rely on permanent magnets. Tailoring permanent magnets towards the specific needs of an application while reduce the content of critical elements is vital for the necessary expansion of green technologies. This project aims at the use of data-driven machine learning, to enhance the basic understanding of magnetization reversal and to facilitate inverse design of magnetic materials. Though prominently used in materials design for magnetic data storage and spin electronics, micromagnetic simulations are hardly scalable to address design questions of bulk materials. An alternative methodology for inverse design is the use of data-driven machine learning. Through assimilation of data arising from high-throughput measurements on combinatorial sputtered magnetic films and from micromagnetic graph networks models that predict hysteresis properties from chemical composition, structure, and processing conditions will be established. In the field of fluid dynamics and structural mechanics graph networks were found to speed up traditional simulations by orders of magnitude. Patterning the film structures give island of a size small enough to be treated with accurate micromagnetic simulations. Thus, data created by both experiments and simulation can be assimilated for the creating of robust and reliable machine learning models. The project will focus on tailoring the properties of (Nd,Dy,La,Ce)FeB magnets with a strongly reduced Nd and Dy content by changing chemical composition and exploring multiphase structures achieved by grain boundary diffusion.