ArticleJournal of applied crystallography2023
Explainable machine learning for diffraction patterns.
Article in Journal of applied crystallography, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Uncertainty aware machine learning for bridging simulation and experiment in high throughput materials characterization.Scientific reports · 2026Article
- Application of machine learning for nanodiamonds shape and surface classification based on X-ray pattern analysis.Scientific reports · 2025Article
- Machine learning for the classification of serial electron diffraction patterns: synthetic data.Acta crystallographica. Section A, Foundations and advances · 2025Article
- The phase-seeding method for solving non-centrosymmetric crystal structures: a challenge for artificial intelligence.Acta crystallographica. Section A, Foundations and advances · 2025Article
- Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images.Journal of applied crystallography · 2024Article
- Introduction to the virtual collection of papers onJournal of applied crystallography · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Serial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as 'hit' and 'miss', respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify the data into hit and miss categories in order to achieve data reduction. The quantitative performance established in previous work indicates that CNNs successfully classify serial crystallography data into desired categories [Ke, Brewster, Yu, Ushizima, Yang & Sauter (2018).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.