التفاصيل البيبلوغرافية
| العنوان: |
Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn--Hilliard-type simulations using autoencoder networks. |
| المؤلفون: |
Timmermann, Sonja, Starostin, Vladimir, Girelli, Anita, Ragulskaya, Anastasia, Rahmann, Hendrik, Reiser, Mario, Begam, Nafisa, Randolph, Lisa, Sprung, Michael, Westermeier, Fabian, Fajun Zhang, Schreiber, Frank, Gutt, Christian |
| المصدر: |
Journal of Applied Crystallography; Aug2022, Vol. 55 Issue 4, p751-757, 7p |
| مصطلحات موضوعية: |
PHOTON correlation, FREE electron lasers, X-rays, PHASE separation, X-ray lasers, DIFFERENTIAL evolution |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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