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

Journal of applied crystallography, 2022-08, Vol.55 (4), p.751-757 [Peer Reviewed Journal]

2022 Sonja Timmermann et al. published by IUCr Journals. ;2022. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Sonja Timmermann et al. 2022 2022 ;ISSN: 1600-5767 ;ISSN: 0021-8898 ;EISSN: 1600-5767 ;DOI: 10.1107/S1600576722004435 ;PMID: 35974741

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Title:
Automated matching of two‐time X‐ray photon correlation maps from phase‐separating proteins with Cahn–Hilliard‐type simulations using auto‐encoder networks
Author: Timmermann, Sonja ; Starostin, Vladimir ; Girelli, Anita ; Ragulskaya, Anastasia ; Rahmann, Hendrik ; Reiser, Mario ; Begam, Nafisa ; Randolph, Lisa ; Sprung, Michael ; Westermeier, Fabian ; Zhang, Fajun ; Schreiber, Frank ; Gutt, Christian
Subjects: auto‐encoders ; Cahn–Hilliard ; Coders ; Correlation ; Evolutionary algorithms ; Evolutionary computation ; Liquid phases ; Machine learning ; Matching ; Phase separation ; Photons ; protein dynamics ; Proteins ; Research Papers ; Simulation ; Synchrotrons ; XPCS ; X‐ray photon correlation spectroscopy
Is Part Of: Journal of applied crystallography, 2022-08, Vol.55 (4), p.751-757
Description: 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. Two‐time correlation maps are classified in a simulation framework using an auto‐encoder network.
Publisher: 5 Abbey Square, Chester, Cheshire CH1 2HU, England: International Union of Crystallography
Language: English
Identifier: ISSN: 1600-5767
ISSN: 0021-8898
EISSN: 1600-5767
DOI: 10.1107/S1600576722004435
PMID: 35974741
Source: Alma/SFX Local Collection

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

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