Automated matching of two-time X-ray photon correlation mans from phase-separating proteins with Cahn-Hilliard-type simulations using auto-encoder networks

Machine learning methods are used for an automated classification of experimental two-time X-ray photon correlation maps from an arrested liquid-liquid phase separation of a protein solution. The correlation maps are matched with correlation maps generated with Cahn-Hilliard-type simulations of liquid-liquid phase separations according to two simulation parameters and in the last step interpreted in the framework of the simulation. The matching routine employs an auto-encoder network and a differential evolution based algorithm. The method presented here 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 with phase field models of phase separation.

Automated summary

Machine learning methods are used to automatically classify X-ray photon correlation maps from protein solution experiments. The correlation maps are matched with simulated maps of liquid-liquid phase separations and interpreted in the simulation framework. This method facilitates the handling of large amounts of dynamic data.