  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.
