Classification of skyrmionic textures and extraction of Hamiltonian parameters via machine learning

Classifying skyrmionic textures and extracting magnetic Hamiltonian parameters represent crucial and challenging pursuits within the realm of two-dimensional (2D) spintronics. In this study, we leverage micromagnetic simulation and machine learning (ML) to theoretically achieve the recognition of nine distinct skyrmionic textures and the extraction of magnetic Hamiltonian parameters from extensive spin texture images in a 2D Heisenberg model. For texture classification, a deep neural network (DNN) trained through transfer learning is proposed to enable the accurate discrimination of nine diverse skyrmionic textures. In parallel, for parameter extraction, based on the textures generated by different Heisenberg exchange stiffnesses (J), Dzyaloshinskii-Moriya strengths (D), and anisotropy constants (K), we employ a multi -input single -output (MISO) deep learning model (handling both images and parameters) and a support vector regression (SVR) model (dealing with Fourier features) to extract the parameters embedded in the spin textures. Our models for classification and extraction demonstrate significant success, achieving an accuracy of 98% (DNN), and R -squared (R2) of 0.90 (MISO) and 0.8 (SVR). Notably, our ML methods prove their effectiveness in distinguishing skyrmionic textures with blurred phase boundaries, thereby contributing to a more nuanced understanding of these textures. Additionally, our models establish a mapping relationship between spin texture images and magnetic parameters, thus validating the feasibility of extracting microscopic mechanisms from experimental images. This finding offers significant guidance for spintronics experiments, emphasizing the potential of ML methods in elucidating intricate details from practical observations.