ReviewJournal of applied crystallography2023
Machine learning for scattering data: strategies, perspectives and applications to surface scattering.
Review 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 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Towards machine-learning-based on-the-fly analysis of neutron reflectometry.Journal of applied crystallography · 2026Article
- Machine-learning-informed scattering correlation analysis of sheared colloids.Journal of applied crystallography · 2025Article
- Benchmarking deep learning for automated peak detection on GIWAXS data.Journal of applied crystallography · 2025Article
- Fast and reliable probabilistic reflectometry inversion with prior-amortized neural posterior estimation.Science advances · 2025Article
- Article
- Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge.Journal of applied crystallography · 2024Article
- Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments.Journal of synchrotron radiation · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.
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What OpenQuestion holds
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.