Project Details
Projekt
Machine learning assisted hysteresis design (B13*)
Subject Area
Computer-Aided Design of Materials and Simulation of Materials Behaviour from Atomic to Microscopic Scale
Term
since 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 405553726
Local magnetization reversal processes at the nm-scale and collective magnetization change in macroscopic samples will be mapped by doing conventional micromagnetic simulations using ML assisted scale bridging modelling and inverse microstructure-based hysteresis design. High-throughput micromagnetic simulations will be carried out for both synthetic theoretical and digitalized experimental microstructures (e.g., taken from APT, TEM, and SEM-EBSD measurements). Such micromagnetic simulations will be informed by combining density functional theory and atomistic spin/lattice dynamics, taking the microstructure-related data as input. The resulting ML surrogate model allows computationally efficient prediction of local reversal fields and can be applied directly in the computational homogenization to evaluate phase transitional features and macroscopic hysteresis. (A01, A03, A05, A06. A07, B01, B11, B12).
DFG Programme
CRC/Transregios
Subproject of
TRR 270:
Hysteresis Design of Magnetic Materials for Efficient Energy Conversion: HoMMage
Applicant Institution
Technische Universität Darmstadt
