110 citations · 582 across the 25 of their papers we have counts for
12 papers · 1 filter
Tailoring the material properties, nanostructure and grain alignment of Alnico magnets through micromagnetic simulations
Anda Elena Stanciu, Johann Fischbacher, Markus Gusenbauer +9
Alnico magnets have gained renewed interest in the search for rare-earth free permanent magnets due to their high thermal stability and magnetisation. However, the limited coercivi…
Exploring the hysteresis properties of nanocrystalline permanent magnets using deep learning
Alexander Kovacs, Lukas Exl, Alexander Kornell +11
We demonstrate the use of model order reduction and neural networks for estimating the hysteresis properties of nanocrystalline permanent magnets from microstructure. With a data-d…
Magnetostatics and micromagnetics with physics informed neural networks
Alexander Kovacs, Lukas Exl, Alexander Kornell +8
Partial differential equations and variational problems can be solved with physics informed neural networks (PINNs). The unknown field is approximated with neural networks. Minimiz…
Computational Design of the Rare-Earth Reduced Permanent Magnets
A. Kovacs, J. Fischbacher, M. Gusenbauer +6
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 cal…
Micromagnetics of rare-earth efficient permanent magnets
Johann Fischbacher, Alexander Kovacs, Markus Gusenbauer +4
The development of permanent magnets containing less or no rare-earth elements is linked to profound knowledge of the coercivity mechanism. Prerequisites for a promising permanent…
Learning magnetization dynamics
Alexander Kovacs, Johann Fischbacher, Harald Oezelt +5
Deep neural networks are used to model the magnetization dynamics in magnetic thin film elements. The magnetic states of a thin film element can be represented in a low dimensional…
Magnetic microstructure machine learning analysis
Lukas Exl, Johann Fischbacher, Alexander Kovacs +6
We use a machine learning approach to identify the importance of microstructure characteristics in causing magnetization reversal in ideally structured large-grained NdFe$_{14}…
Probability distribution of substituted Titanium in RT12 (R = Nd, Sm, T = Fe, Co) structures
Connor Skelland, Thomas Ostler, Samuel Westland +11
We investigated the atomic fill site probability distributions across supercell structures of RT12-xTi (R=Nd, Sm, T=Fe, Co). We use a combined molecular dynamics and Boltzmann dist…
Tuning magnetocrystalline anisotropy of FeSn by alloying
Olga Yu. Vekilova, Bahar Fayyazi, Konstantin P. Skokov +8
The electronic structure, magnetic properties and phase formation of hexagonal ferromagnetic FeSn-based alloys have been studied from first principles and by experiment. The…
On the limits of coercivity in permanent magnets
J. Fischbacher, A. Kovacs, H. Oezelt +14
The maximum coercivity that can be achieved for a given hard magnetic alloy is estimated by computing the energy barrier for the nucleation of a reversed domain in an idealized mic…
Thermal activation in permanent magnets
Simon Bance, Johann Fischbacher, Alexander Kovacs +3
The coercive field of permanent magnets decays with temperature. At non-zero temperature the system can overcome a finite energy barrier through thermal fluctuations. Using finite…
Micromagnetics of shape anisotropy based permanent magnets
Simon Bance, Johann Fischbacher, Thomas Schrefl +3
In the search for rare-earth free permanent magnets, various ideas related to shape anisotropy are being pursued. In this work we assess the limits of shape contributions to the re…