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

Abstract

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.