Speaker
Description
In this talk, I will provide an overview of the current status of electronic structure theory applied to the field of (nano)magnetism. I will discuss how first-principles methods, particularly density functional theory (DFT), enable us to model a wide range of magnetic materials fully grounded in quantum mechanics. I will then address the challenges that arise when the relevant magnetic phenomena involve length or time scales that exceed the reach of standard DFT calculations, and how these are handled through extended frameworks. Among such extensions, I will focus on two state-of-the-art approaches: (i) the atomistic approximation, where an effective spin Hamiltonian -often formulated as a generalized Heisenberg model - is constructed from ab initio parameters; and (ii) the use of machine learning and deep learning techniques to predict magnetic properties directly, bypassing full DFT calculations, or to efficiently parametrize spin Hamiltonians within the atomistic scheme. The presentation aims to provide a concise overview of theoretical strategies that connect quantum-level calculations to experimentally measurable magnetic properties, setting the stage for future collaborative efforts.
