Classification of different magnetic structures from image data using deep neural networks

Abstract

We apply machine learning, specially deep neural network approaches, to train a new model that can perform an effective classification of ferromagnetic, anti-ferromagnetic, skyrmion, anti-skyrmion and spin spiral configurations via supervised learning and also observe how the pre trained models like VGG16, VGG19, ResNet, Inception behave while solving this problem, draw a pattern from it and suggest path for further improving the model. The problem relies in categorization of Magnetic Configurations amongst many from input samples of simulation data to retrieve classified outcome from several different magnetic configurations. The input sample is data achieved from simulations of physical properties of the various magnetic configurations. First CNN is used to classify between the images. Image classifications are mostly carried out using neural networks where data is placed in a graphical structure. The proposed model in this research paper can successfully classify amongst magnetic configurations in real time with data. In our approach, we used a single deep neural network architecture is classify all five types of magnetic structures. All in all, this is a holistic approach for solving the classification problem of magnetic configuration and taking a step into optimizing the model.