Data-Driven Estimation of the interfacial Dzyaloshinskii-Moriya Interaction with Machine Learning
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...
| Authors | Davi Rodrigues, Andrea Meo, Ali Hasan et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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