Extracting local switching fields in permanent magnets using machine learning
This work addresses the need for rapid characterization of permanent magnet quality, but it is incremental as it applies existing machine learning methods to a specific material system.
The authors tackled the problem of linking microstructural features to local coercivity in MnAl-C permanent magnets by using machine learning, achieving predictions of local switching fields from microscopic data within seconds.
Microstructural features play an important role for the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, which is well known for MnAl-C, for example. In this work we show a direct link of microstructural features to the local coercivity of MnAl-C grains by machine learning. A large number of micromagnetic simulations is performed directly from Electron Backscatter Diffraction (EBSD) data using an automated meshing, modeling and simulation procedure. Decision trees are trained with the simulation results and predict local switching fields from new microscopic data within seconds.