Feature extended energy landscape model for interpreting coercivity mechanism

Abstract

AbstractCoercivity is expressed as a complex correlation between magnetisation and microstructures. However, owing to multiple intrinsic origins, coercivity has not been fully understood in the framework of the conventional Ginzburg–Landau theory. Here, we use machine learning to draw a realistic energy landscape of magnetisation reversal to consider missing parameters in the Ginzburg–Landau theory. The energy landscape in the magnetisation reversal process is visualised as a function of features extracted via machine learning; the correlation between the reduced feature space and hysteresis loop is assigned. Features in the lower dimension dataset strongly correlate with magnetisation and are embedded with morphological information. We analyse the energy landscape for simulated and experimental magnetic domain structures; a similar trend is observed. The landscape map enables visualisation of the energy of the system and coercivity as a function of feature space components.