Computational Design of Rare-Earth Reduced Permanent Magnets
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 mat...
Main Authors: | , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2020-02-01
|
Series: | Engineering |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2095809919308689 |
_version_ | 1819106835492241408 |
---|---|
author | Alexander Kovacs Johann Fischbacher Markus Gusenbauer Harald Oezelt Heike C. Herper Olga Yu. Vekilova Pablo Nieves Sergiu Arapan Thomas Schrefl |
author_facet | Alexander Kovacs Johann Fischbacher Markus Gusenbauer Harald Oezelt Heike C. Herper Olga Yu. Vekilova Pablo Nieves Sergiu Arapan Thomas Schrefl |
author_sort | Alexander Kovacs |
collection | DOAJ |
description | 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 |
first_indexed | 2024-12-22T02:44:27Z |
format | Article |
id | doaj.art-3e518bedb17e40aca524e65934fc3e93 |
institution | Directory Open Access Journal |
issn | 2095-8099 |
language | English |
last_indexed | 2024-12-22T02:44:27Z |
publishDate | 2020-02-01 |
publisher | Elsevier |
record_format | Article |
series | Engineering |
spelling | doaj.art-3e518bedb17e40aca524e65934fc3e932022-12-21T18:41:34ZengElsevierEngineering2095-80992020-02-0162148153Computational Design of Rare-Earth Reduced Permanent MagnetsAlexander Kovacs0Johann Fischbacher1Markus Gusenbauer2Harald Oezelt3Heike C. Herper4Olga Yu. Vekilova5Pablo Nieves6Sergiu Arapan7Thomas Schrefl8Department for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt 2700, AustriaDepartment for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt 2700, AustriaDepartment for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt 2700, AustriaDepartment for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt 2700, AustriaDepartment of Physics and Astronomy, Uppsala University, Uppsala 75120, SwedenDepartment of Physics and Astronomy, Uppsala University, Uppsala 75120, SwedenInternational 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 RepublicInternational 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 RepublicDepartment 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, Micromagneticshttp://www.sciencedirect.com/science/article/pii/S2095809919308689 |
spellingShingle | Alexander Kovacs Johann Fischbacher Markus Gusenbauer Harald Oezelt Heike C. Herper Olga Yu. Vekilova Pablo Nieves Sergiu Arapan Thomas Schrefl Computational Design of Rare-Earth Reduced Permanent Magnets Engineering |
title | Computational Design of Rare-Earth Reduced Permanent Magnets |
title_full | Computational Design of Rare-Earth Reduced Permanent Magnets |
title_fullStr | Computational Design of Rare-Earth Reduced Permanent Magnets |
title_full_unstemmed | Computational Design of Rare-Earth Reduced Permanent Magnets |
title_short | Computational Design of Rare-Earth Reduced Permanent Magnets |
title_sort | computational design of rare earth reduced permanent magnets |
url | http://www.sciencedirect.com/science/article/pii/S2095809919308689 |
work_keys_str_mv | AT alexanderkovacs computationaldesignofrareearthreducedpermanentmagnets AT johannfischbacher computationaldesignofrareearthreducedpermanentmagnets AT markusgusenbauer computationaldesignofrareearthreducedpermanentmagnets AT haraldoezelt computationaldesignofrareearthreducedpermanentmagnets AT heikecherper computationaldesignofrareearthreducedpermanentmagnets AT olgayuvekilova computationaldesignofrareearthreducedpermanentmagnets AT pablonieves computationaldesignofrareearthreducedpermanentmagnets AT sergiuarapan computationaldesignofrareearthreducedpermanentmagnets AT thomasschrefl computationaldesignofrareearthreducedpermanentmagnets |