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Extracting local nucleation fields in permanent magnets using machine learning

npj Computational Materials 2020年第1期6卷 924-933页

作者： Markus Gusenbauer Harald Oezelt johann fischbacher Alexander Kovacs Panpan Zhao Thomas George Woodcock Thomas Schrefl Department for Integrated Sensor Systems Danube University KremsKrems an der DonauAustria Institute for Metallic Materials Leibniz IFW DresdenDresdenGermany

Microstructural features play an important role in the quality of permanent *** coercivity is greatly influenced by crystallographic defects,like twin boundaries,as is well known for *** would be very useful to be able to predict the macroscopic coercivity from microstructure *** this is not possible now,in the present work we examine a related question,namely the prediction of simulated nucleation fields of a quasi-three-dimensional(rescaled and extruded)system constructed from a two-dimensional image.

关键词： microstructure permanent magnets

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Full-spin-wave-scaled stochastic micromagnetism for mesh-independent simulations of ferromagnetic resonance and reversal

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npj Computational Materials 2022年第1期8卷 315-323页

作者： Harald Oezelt Luman Qu Alexander Kovacs johann fischbacher Markus Gusenbauer Roman Beigelbeck Dirk Praetorius Masao Yano Tetsuya Shoji Akira Kato Roy Chantrell Michael Winklhofer Gergely T.Zimanyi Thomas Schrefl Department for Integrated Sensor Systems Danube University Krems2700Wiener NeustadtAustria Department of Physics University of CaliforniaDavisCA95616USA Christian Doppler Laboratory for Magnet design through physics informed machine learning 2700Wiener NeustadtAustria Institute for Analysis and Scientific Computing Vienna University of Technology1040WienAustria Toyota Motor Corp. Toyota City471-8572Japan Department of Physics University of YorkYorkYO105DDUK Institute for Biology and Environmental Sciences IBU Carl von Ossietzky University of Oldenburg26129OldenburgGermany

In this paper,we address the problem that standard stochastic Landau-Lifshitz-Gilbert(sLLG)simulations typically produce results that show unphysical mesh-size *** root cause of this problem is that the effects of spin-wave fluctuations are ignored in *** propose to represent the effect of these fluctuations by a full-spin-wave-scaled stochastic LLG,or FUSSS LLG *** FUSSS LLG,the intrinsic parameters of the sLLG simulations are first scaled by scaling factors that integrate out the spin-wave fluctuations up to the mesh size,and the sLLG simulation is then performed with these scaled *** developed FUSSS LLG by studying the Ferromagnetic Resonance(FMR)in Nd_(2)Fe_(14)B *** nominal scaling greatly reduced the mesh size dependence relative to *** then performed three tests and validations of our FUSSS LLG with this modified scaling.(1)We studied the same FMR but with magnetostatic fields included.(2)We simulated the total magnetization of the Nd_(2)Fe_(14)B cube.(3)We studied the effective,temperature-and sweeping rate-dependent coercive field of the *** all three cases,we found that FUSSS LLG delivered essentially mesh-size-independent results,which tracked the theoretical expectations better than unscaled *** by these successful validations,we propose that FUSSS LLG provides marked,qualitative progress towards accurate,high precision modeling of micromagnetics in hard,permanent magnets.

关键词： stochastic wave scaling

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Computational Design of Rare-Earth Reduced Permanent Magnets

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Engineering 2020年第2期6卷 148-153页

作者： Alexander Kovacs johann fischbacher Markus Gusenbauer Harald Oezelt Heike C.Herper Olga Yu.Vekilova Pablo Nieves Sergiu Arapan Thomas Schrefl Department for Integrated Sensor Systems Danube University KremsWiener Neustadt 2700Austria Department of Physics and Astronomy Uppsala UniversityUppsala 75120Sweden International Research Centre in Critical Raw Materials for Advanced Industrial Technologies University of BurgosBurgos 09001Spain IT4Innovations VSB-Technical University of OstravaOstrava-Poruba 70833Czech Republic

Multiscale simulation is a key research tool in the quest for new permanent *** 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 ***(Fe)-rich magnetic phases suitable for permanent magnets can be found by means of adaptive genetic *** intrinsic properties computed by ab initio simulations are used as input for micromagnetic simulations of the hysteresis properties of permanent magnets with a realistic *** 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 *** property relations of synthetic permanent magnets were computed for several candidate hard magnetic *** 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).

关键词： Rare-earth Permanent magnets Micromagnetics

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