ArticleScientific reports2021
Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry.
Article in Scientific reports, 2021. 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
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Who cites it
7 citing papers in PubMed.
- Towards machine-learning-based on-the-fly analysis of neutron reflectometry.Journal of applied crystallography · 2026Article
- Extracting Thin Film Structures of Energy Materials Using Transformers.ACS physical chemistry Au · 2025Article
- 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
- Machine learning for scattering data: strategies, perspectives and applications to surface scattering.Journal of applied crystallography · 2023Review
- Neutron Imaging and Learning Algorithms: New Perspectives in Cultural Heritage Applications.Journal of imaging · 2022Review
- Faster and lower-dose X-ray reflectivity measurements enabled by physics-informed modeling and artificial intelligence co-refinement.Journal of applied crystallography · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Neutron reflectometry (NR) allows us to probe into the structure of the surfaces and interfaces of various materials such as soft matters and magnetic thin films with a contrast mechanism dependent on isotopic and magnetic states. The neutron beam flux is relatively low compared to that of other sources such as synchrotron radiation; therefore, there has been a strong limitation in the time-resolved measurement and further advanced experiments such as surface imaging. This study aims at the development of a methodology to enable the structural analysis by the NR data with a large statistical error acquired in a short measurement time. The neural network-based method predicts the true NR profile from the data with a 20-fold lower signal compared to that obtained under the conventional measurement condition. This indicates that the acquisition time in the NR measurement can be reduced by more than one order of magnitude. The current method will help achieve remarkable improvement in temporally and spatially resolved NR methods to gain further insight into the surface and interfaces of materials.
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Registered trials
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