data i miejsce: 11.06.2025 godz. 12:15, sala 106 bud A-29
temat: Interpreting FMR experiments: Tradition vs. machine learning
prelegent: dr Aleksandra Napierała-Batygolska (Zakład Fizyki Materiałów Funkcjonalnych, UAM w Poznaniu)

Abstract:
The phenomenon of ferromagnetic resonance (FMR) is still being widely used for determining the magnetocrystalline anisotropy constants of magnetic materials. However, there are three methods of analyzing experimental results: 1. using the Kittel equation [Phys. Rev. 73, 155 (1948)], 2. using the Smith-Beljers equation [Philips Res. Rep. 10, 113 (1955)], and the latest method - using machine learning techniques [Phys. Rev. B 98, 144415 (2018)]. We compare the results of applying these three methods to the analysis of the FMR experiment carried out for magnetite [Phys. Rev, 78, 449 (1950)], epitaxial layers magnetic semiconductor (Ga,Mn)As on (113) GaAs [Phys. Rev. B 81, 155203 (2010), Phys.Rev. B 91, 184403 (2018)] . The results of our analysis indicate that the use of machine learning offers a significant advantage over other approaches, as confirmed in [Scientific Reports, 15, 11277 (2025)]. Our analysis allows us to unambiguously determine the spatial distribution of free energy (and therefore magnetocrystalline energy). This applies in particular to the situation when the FMR measurements were carried out in different crystallographic planes and the directions of the sample’s crystallographic axes do not coincide with the directions of the shape anisotropy. Then the use of machine learning techniques becomes indispensable.