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We use machine learning methods for an automated classification ofexperimental XPCS two-time correlation functions from an arrested liquid-liquidphase separation of a protein solution. We couple simulations based on theCahn-Hilliard equation with a glass transition scenario and classify themeasured correlation maps automatically according to quench depth and criticalconcentration at a glass/gel transition. We introduce routines andmethodologies using an autoencoder network and a differential evolution basedalgorithm for classification of the measured correlation functions. The herepresented method is a first step towards handling large amounts of dynamic datameasured at high brilliance synchrotron and X-ray free-electron laser sourcesfacilitating fast comparison to phase field models of phase separation.

Automated matching of two-time X-ray photon correlation maps from protein dynamics with Cahn-Hilliard type simulations using autoencoder networks