Classi cation of magnetic configurations using machine learning algorithms

Abstract

Machine learning is used to carry out e cient studies and analyses in the eld of condensed matter physics. We propose comprehensive machine learning approaches that would classify between magnetic structures. We propose models that are trained on data that has been generated on 3D lattices of Heisenberg model using the physical properties of respective magnetic structures. Models are designed based on three types of classi cations, rst classi cation is done between topologically-protected structures, second on non-topologically-protected structures, thirdly on all structures collectively. To achieve this, convolutional neural network (CNN) and support vector machine (SVM) with principle component analysis (PCA) algorithms have been used. We then make a comparative analysis and nd the most optimal solution. The results show that CNN provides the highest accuracy in the classi cation of topological and non-topological magnetic con gurations.