  The Dzyaloshinskii-Moriya interaction (DMI), which is the antisymmetric part
of the exchange interaction between neighboring local spins, winds the spin
manifold and can stabilize non-trivial topological spin textures. Since
topology is a robust information carrier, characterization techniques that can
extract the DMI magnitude are important for the discovery and optimization of
spintronic materials. Existing experimental techniques for quantitative
determination of DMI, such as high-resolution magnetic imaging of spin textures
and measurement of magnon or transport properties, are time consuming and
require specialized instrumentation. Here we show that a convolutional neural
network can extract the DMI magnitude from minor hysteresis loops, or magnetic
"fingerprints" of a material. These hysteresis loops are readily available by
conventional magnetometry measurements. This provides a convenient tool to
investigate topological spin textures for next-generation information
processing.
