Coarse-Graining and Classifying Massive High-Throughput XFEL Datasets of Crystallization in Supercooled Water

Ice crystallization in supercooled water is a complex phenomenon with far-reaching implications across scientific disciplines, including cloud formation physics and cryopreservation. Experimentally studying such complexity can be a highly data-driven and data-hungry endeavor because of the need to record rare events that cannot be triggered on demand. Here, we describe such an experiment comprising 561 million images of X-ray free-electron laser (XFEL) diffraction patterns (2.3 PB raw data) spanning the disorder-to-order transition in micrometer-sized supercooled water droplets. To effectively analyze these patterns, we propose a data reduction (i.e., coarse-graining) and dimensionality reduction (i.e., principal component analysis) strategy. We show that a simple set of criteria on this reduced dataset can efficiently classify these patterns in the absence of reference diffraction signatures, which we validated using more precise but computationally expensive unsupervised machine learning techniques. For hit-finding, our strategy attained 98% agreement with our cross-validation. We speculate that these strategies may be generalized to other types of large high-dimensional datasets generated at high-throughput XFEL facilities.

Authors

Institutions

Publication Details

Journal
Crystals
Published
2025-08-19
DOI
https://doi.org/10.3390/cryst15080734
Citations
1
Primary Topic
Hydrocarbon exploration and reservoir analysis
Type
article
Field-Weighted Citation Impact
0.86

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Coarse-Graining and Classifying Massive High-Throughput XFEL Datasets of Crystallization in Supercooled Water

Connie Darmanin, Tokushi Sato, O. Turkot, Sebastian Cardoch et al.
1 citations
Crystals
Hydrocarbon exploration and reservoir analysis
0.86
article

Coarse-Graining and Classifying Massive High-Throughput XFEL Datasets of Crystallization in Supercooled Water

Connie Darmanin, Tokushi Sato, O. Turkot, Sebastian Cardoch, Diogo V. M. Melo, Henry N. Chapman, Raphaël de Wijn, J. Malka, Philipp Schmidt, Mohammad Vakili, Joana Valério, Jayanath Koliyadu, Nicuşor Tı̂mneanu, Tej Varma Yenupuri, Johan Bielecki, Michael P. Hassett, Andrew Formosa, Ruslan P. Kurta, Andrew V. Martin, Seonmyeong Kim, E. V. Sobolev, Tim B. Berberich, Carl Caleman, Emiliano De Santis, S. Bajt, Filipe R. N. C. Maia, Romain Letrun, Stefan Paporakis, Patrick Adams, Gun‐Sik Park, Bjorn Hammarström, N. Duane Loh, Jonas A. Sellberg, Brian Abbey, Marco Kloos, Tomas André, Tong You, Ervin S. H. Chia
article en
1 citations

Abstract

Ice crystallization in supercooled water is a complex phenomenon with far-reaching implications across scientific disciplines, including cloud formation physics and cryopreservation. Experimentally studying such complexity can be a highly data-driven and data-hungry endeavor because of the need to record rare events that cannot be triggered on demand. Here, we describe such an experiment comprising 561 million images of X-ray free-electron laser (XFEL) diffraction patterns (2.3 PB raw data) spanning the disorder-to-order transition in micrometer-sized supercooled water droplets. To effectively analyze these patterns, we propose a data reduction (i.e., coarse-graining) and dimensionality reduction (i.e., principal component analysis) strategy. We show that a simple set of criteria on this reduced dataset can efficiently classify these patterns in the absence of reference diffraction signatures, which we validated using more precise but computationally expensive unsupervised machine learning techniques. For hit-finding, our strategy attained 98% agreement with our cross-validation. We speculate that these strategies may be generalized to other types of large high-dimensional datasets generated at high-throughput XFEL facilities.

CrystalsVol. 15(8)
Uppsala University (SE), Seoul National University (KR), Universität Hamburg (DE), National University of Singapore (SG), La Trobe University (AU), Deutsches Elektronen-Synchrotron DESY (DE), Center for Free-Electron Laser Science (DE), RMIT University (AU), KTH Royal Institute of Technology (SE), European X-Ray Free-Electron Laser (DE)
Göran Gustafssons Stiftelser, Ministry of Education - Singapore, Carl Tryggers Stiftelse för Vetenskaplig Forskning, National Research Foundation of Korea, Vetenskapsrådet, Australian Research Council
Openalex Percentile: Top 26%
Hydrocarbon exploration and reservoir analysis
0.86
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.