  We use machine learning methods for an automated classification of
experimental XPCS two-time correlation functions from an arrested liquid-liquid
phase separation of a protein solution. We couple simulations based on the
Cahn-Hilliard equation with a glass transition scenario and classify the
measured correlation maps automatically according to quench depth and critical
concentration at a glass/gel transition. We introduce routines and
methodologies using an autoencoder network and a differential evolution based
algorithm for classification of the measured correlation functions. The here
presented method is a first step towards handling large amounts of dynamic data
measured at high brilliance synchrotron and X-ray free-electron laser sources
facilitating fast comparison to phase field models of phase separation.

Soft Condensed Matter;Materials Science