B13
Machine learning assisted hysteresis design

Overview

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)

Team

  Name Contact
Prof. Dr. Bai-Xiang Xu
Prof. Dr. Bai-Xiang Xu
+49 (0)6151 16-21906
L1|08 419
Jun. Prof. Dr. Hongbin Zhang
Prof. Dr. Hongbin Zhang
+49 (0)6151 16-23135
L2|01 254