抄録
The determination of material parameters is significantly important in material science, which is often a challenging task. Recently, advancements have shown that magnetic parameters, such as the Dzyaloshinskii–Moriya interaction (DMI), can be estimated from a magnetic domain image using machine learning (ML). This development suggests a potential shift in how magnetic parameters are determined, moving away from traditional measurement techniques to more innovative methods involving image-based inputs processed by ML. In previous studies, the test images used for estimation always matched the training images in size. However, since the image size is contingent on the microscopy technique used, the ability to accurately estimate parameters from images of varying sizes is essential. Here, we investigated the feasibility of estimating the DMI constant and saturation magnetization from magnetic domain images of different sizes using ML. We demonstrated that it is possible to estimate these parameters even when the imaging sizes differ between training and test datasets. Additionally, our comparison of the estimation accuracy for the DMI constant and saturation magnetization revealed that the tolerance for differences in the image size varies depending on the specific parameter being estimated. These findings could have a significant impact on the future methods of determining magnetic parameters.