Title: Probing Hidden Spin Order with Interpretable Machine Learning,
Abstract: The search of unconventional magnetic and non-magnetic states is a major
topic in the study of frustrated magnetism. Canonical examples of those states
include various spin liquids and spin nematics. However, discerning their
existence and the correct characterization is usually challenging. Here we
introduce a machine-learning protocol that can identify general nematic order
and their order parameter from seemingly featureless spin configurations, thus
providing comprehensive insight on the presence or absence of hidden orders. We
demonstrate the capabilities of our method by extracting the analytical form of
nematic order parameter tensors up to rank 6. This may prove useful in the
search for novel spin states and for ruling out spurious spin liquid
candidates.