Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrate deep learning can quantify the magnetic Hamiltonian from magnetic domain images. To train the deep neural network, we generated domain configurations with Monte Carlo method. The errors from the estimations was analyzed with statistical methods and confirmed the network was successfully trained to relate the Hamiltonian parameters with magnetic structure characteristics. The network was applied to estimate experimentally observed domain images. The results are consistent with the reported results, which verifies the effectiveness of our methods. On the basis of our study, we anticipate that the deep learning techniques make a bridge to connect the experimental and theoretical approaches not only in magnetism but also throughout any scientific research.

了解磁性系统中的自旋纹理对于自旋电子学极为重要，并且通过实验确定的自旋来推断磁性哈密顿量参数至关重要。它可以在理论和实验结果之间提供更好的互补关系。我们证明深度学习可以从磁畴图像量化磁哈密顿量。为了训练深度神经网络，我们使用蒙特卡洛方法生成了域配置。用统计方法分析了估计的误差，并确认网络已成功训练以将哈密顿参数与磁结构特征相关联。该网络被用于估计实验观察到的域图像。结果与报道的结果一致，证明了我们方法的有效性。