3 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2026
Altermagnetic-Like Behavior and Enhanced Coercivity in Ferrimagnets at a Critical Point of an Extended Néel-Diagram
Qais Ali, Anna Grünebohm, Halil Ibrahim Sözen +3
We generalize the classic Néel diagram for ferrimagnets within a mean-field framework and reveal a critical point at which full magnetic compensation is maintained below the Curie…
physics.comp-ph2025
Graph Neural Networks to Predict Coercivity of Hard Magnetic Microstructures
Heisam Moustafa, Alexander Kovacs, Johann Fischbacher +5
Graph neural networks (GNN) are a promising tool to predict magnetic properties of large multi-grain structures, which can speed up the search for rare-earth free permanent magnets…