  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.
