  Understanding and interpreting dynamics of functional materials \textit{in
situ} is a grand challenge in physics and materials science due to the
difficulty of experimentally probing materials at varied length and time
scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited
for characterizing materials dynamics over wide-ranging time scales, however
spatial and temporal heterogeneity in material behavior can make interpretation
of experimental XPCS data difficult. In this work we have developed an
unsupervised deep learning (DL) framework for automated classification and
interpretation of relaxation dynamics from experimental data without requiring
any prior physical knowledge of the system behavior. We demonstrate how this
method can be used to rapidly explore large datasets to identify samples of
interest, and we apply this approach to directly correlate bulk properties of a
model system to microscopic dynamics. Importantly, this DL framework is
material and process agnostic, marking a concrete step towards autonomous
materials discovery.
