  Multiscale simulation is a key research tool for the quest for new permanent
magnets. Starting with first principles methods, a sequence of simulation
methods can be applied to calculate the maximum possible coercive field and
expected energy density product of a magnet made from a novel magnetic material
composition. Fe-rich magnetic phases suitable for permanent magnets can be
found by adaptive genetic algorithms. The intrinsic properties computed by ab
initio simulations are used as input for micromagnetic simulations of the
hysteresis properties of permanent magnets with realistic structure. Using
machine learning techniques, the magnet's structure can be optimized so that
the upper limits for coercivity and energy density product for a given phase
can be estimated. Structure property relations of synthetic permanent magnets
were computed for several candidate hard magnetic phases. The following pairs
(coercive field (T), energy density product (kJ/m3)) were obtained for
Fe3Sn0.75Sb0.25: (0.49, 290), L10 FeNi: (1, 400), CoFe6Ta: (0.87, 425), and
MnAl: (0.53, 80).
