38 citations · 39 across the 6 of their papers we have counts for
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Modelling magnetic material properties with uncertainty-aware neural networks
Clemens Wager, Heisam Moustafa, Alexander Kovacs +10
Machine learning is increasingly applied to accelerate the discovery of novel materials by exploring large compositional and structural design spaces. Yet, the scarcity of high-qua…
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…
Data Assimilation Method for Experimental and First-Principles Data: Finite-Temperature Magnetization of (Nd,Pr,La,Ce)(Fe,Co,Ni)B
Yosuke Harashima, Keiichi Tamai, Shotaro Doi +8
We propose a data-assimilation method for evaluating the finite-temperature magnetization of a permanent magnet over a high-dimensional composition space. Based on a general framew…
Optimal uni-axial ferromagnetism in (La,Ce)FeB for permanent magnets
Munehisa Matsumoto, Masaaki Ito, Noritsugu Sakuma +3
Prospects for light-rare-earth-based permanent magnet compound RFeB (R=LaCe with ) are inspected from first principles referring to the lat…
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…
Grain-size dependent demagnetizing factors in permanent magnets
Simon Bance, Bernhard Seebacher, Thomas Schrefl +10
The coercive field of permanent magnets decreases with increasing grain size. The grain size dependence of coercivity is explained by a size dependent demagnetizing factor. In Dy f…