ArticleJournal of applied crystallography2022
Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn-Hilliard-type simulations using auto-encoder networks.
Article in Journal of applied crystallography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Application of machine learning for nanodiamonds shape and surface classification based on X-ray pattern analysis.Scientific reports · 2025Article
- AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy.Nature communications · 2024Article
- Introduction to the virtual collection of papers onJournal of applied crystallography · 2024Article
- Exploring non-equilibrium processes and spatio-temporal scaling laws in heated egg yolk using coherent X-rays.Nature communications · 2023Article
- Machine learning for scattering data: strategies, perspectives and applications to surface scattering.Journal of applied crystallography · 2023Review
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13 authors.
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Abstract
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.
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