7 papers
Beyond Stoner-Wohlfarth: Machine-Learning Models and Symbolic Regression of Hard-Magnet Properties
Samuel J. R. Holt, Christina Winkler, Timoteo Colnaghi +7
Predicting the extrinsic properties from hysteresis loops of a magnetic grain, namely the coercive field, remanent magnetisation, and maximum energy product, from its intrinsic mic…
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…
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…
Modeling liquid-mediated interactions for close-to-substrate magnetic microparticle transport in dynamic magnetic field landscapes
Markus Gusenbauer, Rico Huhnstock, Alexander Kovacs +3
Understanding the on-chip motion of magnetic particles in a microfluidic environment is key to realizing magnetic particle-based Lab-on-a-chip systems for medical diagnostics. In t…
Effect of interface on magnetic exchange coupling in Co/Ru/Co trilayer: from ab-initio simulations to micromagnetics
Sergiu Arapan, Jan Priessnitz, Alexander Kovacs +5
Interfaces play a substantial role for the functional properties of structured magnetic materials and magnetic multilayers. Modeling the functional behavior of magnetic materials r…
Realization of inverse-design magnonic logic gates
Noura Zenbaa, Fabian Majcen, Claas Abert +6
Magnonic logic gates represent a crucial step toward realizing fully magnonic data processing systems without reliance on conventional electronic or photonic elements. Recently, a…