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
• Project Heads: Professorin Dr.-Ing. Bai-Xiang Xu; Professor Hongbin Zhang, Ph.D.