Computational Design of the Rare-Earth Reduced Permanent Magnets

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 ma...

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Bibliographic Details
Published inarXiv.org
Main Authors Kovacs, A, Fischbacher, J, Gusenbauer, M, Oezelt, H, Herper, H C, O Yu Vekilova, Nieves, P, Arapan, S, Schrefl, T
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.03.2019
Subjects
Adaptive algorithms
Coercivity
Computation
Computer simulation
First principles
Flux density
Genetic algorithms
Iron
Machine learning
Magnetic materials
Magnetic properties
Magnetism
Permanent magnets
Rare earth elements
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Summary: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).
ISSN:2331-8422