Parameterization of the Stoner-Wohlfarth model of magnetic hysteresis

Abstract

The Stoner-Wohlfarth is the most used model of magnetic hysteresis, but its computation is time-consuming. We use machine learning to approximate piecewise this model by easy-to-compute analytic functions. Our parametrization is suitable for fast quantitative evaluations and fitting experimental data, which we exemplify.

5 pages, 4 figures

References in corpus (6)

• AiiDA: Automated Interactive Infrastructure and Database for Computational Science
• FeRh groundstate and martensitic transformation
• Coexistence pressure for a martensitic transformation from theory and experiment: revisiting the bcc-hcp transition of iron under pressure
• Reliable thermodynamic estimators for screening multicaloric materials
• Magneto-structural transformations via a solid-state nudged elastic band method: Application to iron under pressure
• Titanium $\alpha -\omega$ phase transformation pathway and a predicted metastable structure