Estimation of TbCo composition from local-minimum-energy magnetic images taken by magneto-optical Kerr effect microscope by using machine learning

Abstract

Recently, the incorporation of machine learning (ML) has heralded significant advancements in materials science. For instance, in spintronics, it has been shown that magnetic parameters, such as the Dzyaloshinskii–Moriya interaction, can be estimated from magnetic domain images using ML. Magnetic materials exhibit hysteresis, leading to numerous magnetic states with locally minimized energy (LME) even within a single sample. However, it remains uncertain whether these parameters can be derived from LME states. In our research, we explored the estimation of material parameters from an LME magnetic state using a convolutional neural network. We introduced a technique to manipulate LME magnetic states, combining the ac demagnetizing method with the magneto-optical Kerr effect. By applying this method, we generated multiple LME magnetic states from a single sample and successfully estimated its material composition. Our findings suggest that ML emphasizes not the global domain structures that are readily perceived by humans but the more subtle local domain structures that are often overlooked. Adopting this approach could potentially facilitate the estimation of magnetic parameters from any state observed in experiments, streamlining experimental processes in spintronics.