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

Classifying skyrmionic textures and extracting magnetic Hamiltonian parameters are fundamental and demanding endeavors within the field of two-dimensional (2D) spintronics. By using micromagnetic simulation and machine learning (ML) methods, we theoretically realize the recognition of nine skyrmionic textures and the mining of magnetic Hamiltonian parameters from massive spin texture images in 2D Heisenberg model. For textures classification, a deep neural network (DNN) trained according to transfer learning is proposed to distinguish nine different skyrmionic textures. For parameters extraction, based on the textures generated by different Heisenberg exchange stiffness (J), Dzyaloshinskii-Moriya strength (D), and anisotropy constant (K), we apply a multi-input single-output (MISO) deep learning model (handling with both images and parameters) and a support vector regression (SVR) model (dealing with Fourier features) to extract the parameters embedded in the spin textures. The models for classification and extraction both achieve great results with the accuracy of 98% (DNN),90% (MISO) and 80% (SVR). Importantly, via our ML methods, the skyrmionic textures with blurred phase boundaries can be effectively distinguished, and the concluded formation conditions of various skyrmionic textures, especially the skyrmion crystal, are consistent with previous reports. Besides, our models demonstrate the mapping relationship between spin texture images and magnetic parameters, which proves the feasibility of extracting microscopic mechanisms from experimental images and has guiding significance for the experiments of spintronics.