Data-Driven Estimation of the interfacial Dzyaloshinskii-Moriya Interaction with Machine Learning

TL;DR

This paper develops a convolutional neural network trained on micromagnetic data to accurately and robustly estimate interfacial Dzyaloshinskii-Moriya interaction strength from magnetic bubble domain images, even with noise and low resolution.

Contribution

It introduces a machine learning approach that reliably infers DMI from bubble textures, overcoming limitations of traditional experimental methods.

Findings

The neural network accurately predicts DMI values outside the training range.

The model is robust against noise, inhomogeneity, and low-resolution images.

Machine learning can serve as a fast, quantitative tool for magnetic texture characterization.

Abstract

Machine learning offers powerful tools to support experimental techniques, particularly for extracting latent features from large datasets. In magnetic materials, accurately estimating the interfacial Dzyaloshinskii-Moriya interaction strength remains challenging, as existing experimental methods often rely on indirect measurements and can yield inconsistent results across techniques. Because this interaction is often extracted experimentally from bubble domain expansion, we investigate whether bubble textures alone contain sufficient and reliable information for data driven DMI inference. We therefore develop a compact convolutional neural network trained on a comprehensive micromagnetic dataset of magnetic bubble domains designed to emulate magneto optical Kerr effect imaging, including structural non uniformity, additive noise, and image pixelation. The proposed network demonstrates strong robustness against sample inhomogeneities, noise, and reduced spatial resolution. Furthermore, it exhibits reliable generalization by accurately predicting DMI values outside the trained interval. These results support the use of machine learning as a fast and quantitative tool to characterize magnetic textures with interfacial DMI.