  The interplay of magnetic interactions in chiral multilayer films gives rise
to nanoscale topological spin textures, which 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 the parent magnetic interactions. Here we present
a machine learning-based approach to determine the key interactions --
symmetric exchange, chiral exchange, and anisotropy -- governing chiral domain
phenomenology in multilayers. 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 physical interdependencies between
them. When applied to microscopy data acquired across samples, our
model-predicted parameter trends are consistent with 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.
