Quantifying the Magnetic Interactions Governing Chiral Spin Textures Using Deep Neural Networks

The interplay of magnetic interactions in chiral multilayer films gives rise to nanoscale topological spin textures that form attractive elements for next-generation computing. Quantifying these interactions requires several specialized, time-consuming, and resource-intensive experimental techniques. Imaging of ambient domain configurations presents a promising avenue for high-throughput extraction of parent magnetic interactions. Here, we present a machine learning (ML)-based approach to simultaneously determine the key magnetic interactions-symmetric exchange, chiral exchange, and anisotropy-governing the chiral domain phenomenology in multilayers, using a single binarized image of domain configurations. Our convolutional neural network model, trained and validated on over 10,000 domain images, achieved R-2 > 0.85 in predicting the parameters and independently learned the physical interdependencies between magnetic parameters. When applied to microscopy data acquired across samples, our model-predicted parameter trends are consistent with those of independent experimental measurements. These results establish ML-driven techniques as valuable, high-throughput complements to conventional determination of magnetic interactions and serve to accelerate materials and device development for nanoscale electronics.