Computational Design of Rare-Earth Reduced Permanent Magnets
Alexander Kovacs,
Johann Fischbacher,
Markus Gusenbauer,
Harald Oezelt,
Heike C. Herper,
Olga Yu. Vekilova,
Pablo Nieves,
Sergiu Arapan,
Thomas Schrefl
Affiliations
Alexander Kovacs
Department for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt 2700, Austria
Johann Fischbacher
Department for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt 2700, Austria
Markus Gusenbauer
Department for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt 2700, Austria
Harald Oezelt
Department for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt 2700, Austria
Heike C. Herper
Department of Physics and Astronomy, Uppsala University, Uppsala 75120, Sweden
Olga Yu. Vekilova
Department of Physics and Astronomy, Uppsala University, Uppsala 75120, Sweden
Pablo Nieves
International Research Centre in Critical Raw Materials for Advanced Industrial Technologies, University of Burgos, Burgos 09001, Spain; IT4Innovations, VŠB-Technical University of Ostrava, Ostrava-Poruba 70833, Czech Republic
Sergiu Arapan
International Research Centre in Critical Raw Materials for Advanced Industrial Technologies, University of Burgos, Burgos 09001, Spain; IT4Innovations, VŠB-Technical University of Ostrava, Ostrava-Poruba 70833, Czech Republic
Thomas Schrefl
Department for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt 2700, Austria; Corresponding author.
Multiscale simulation is a key research tool in 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. Iron (Fe)-rich magnetic phases suitable for permanent magnets can be found by means of 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 a 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·m−3)) were obtained for iron-tin-antimony (Fe3Sn0.75Sb0.25): (0.49, 290), L10-ordered iron-nickel (L10 FeNi): (1, 400), cobalt-iron-tantalum (CoFe6Ta): (0.87, 425), and manganese-aluminum (MnAl): (0.53, 80). Keywords: Rare-earth, Permanent magnets, Micromagnetics
