Machine learning offers faster, more reliable analysis of Fermi surfaces in search of spintronic materials
The search for next-generation electronic materials often starts with studying the Fermi surface, which serves as a map of a material's electronic structure. Its shape varies with crystal structure, composition, and electronic band arrangement, directly impacting properties such as carrier density,...
Image: Phys.org
The search for next-generation electronic materials often starts with studying the Fermi surface, which serves as a map of a material's electronic structure. Its shape varies with crystal structure, composition, and electronic band arrangement, directly impacting properties such as carrier density, magnetic behavior, and spin polarization. This makes it a crucial tool for understanding and engineering new materials.
Originally published at Phys.org
