Journal article 26 views 4 downloads
Towards machine-learning-based on-the-fly analysis of neutron reflectometry
Anne Rentzsch 
,
Valentin Munteanu ,
Oliver Odira Anyanor,
Shreya Shah ,
Philipp Gutfreund ,
Rémi Perenon,
Anthony Higgins ,
Vladimir Starostin ,
Alexander Hinderhofer ,
Dmitry Lapkin ,
Frank Schreiber
,
Valentin Munteanu ,
Oliver Odira Anyanor,
Shreya Shah ,
Philipp Gutfreund ,
Rémi Perenon,
Anthony Higgins ,
Vladimir Starostin ,
Alexander Hinderhofer ,
Dmitry Lapkin ,
Frank Schreiber
Journal of Applied Crystallography, Volume: 59, Issue: 3
Swansea University Author:
Anthony Higgins
DOI (Published version): 10.1107/s1600576726002657
Abstract
Reflectometry experiments can benefit substantially from recent advances in machine learning, enabling real-time data analysis, informed decision making during measurements, optimized experimental conditions and ultimately closed-loop experimental workflows. While most prior automation efforts have...
| Published in: | Journal of Applied Crystallography |
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| ISSN: | 1600-5767 |
| Published: |
International Union of Crystallography (IUCr)
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71909 |
| Abstract: |
Reflectometry experiments can benefit substantially from recent advances in machine learning, enabling real-time data analysis, informed decision making during measurements, optimized experimental conditions and ultimately closed-loop experimental workflows. While most prior automation efforts have focused on X-ray reflectometry, neutron reflectometry can also benefit to the same extent. We report the first machine-learning-based pipeline for real-time neutron reflectometry deployed at the Institut Laue–Langevin (ILL), Grenoble, France. The system integrates the reflectorch package into the data acquisition workflow using the IT infrastructure of the facility. This enables analysis that is two orders of magnitude faster than conventional tools, allowing real-time estimation of physical parameters with associated uncertainties and feedback through a graphical user interface. The pipeline has been successfully tested at the ILL, and can be adapted to work at other neutron facilities seeking to enable real-time feedback-driven reflectometry analysis. |
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| Keywords: |
neutron reflectometry; machine learning; online data analysis; experiment automation |
| College: |
Faculty of Science and Engineering |
| Funders: |
We thank the Bundesministerium fu¨r Bildung und Forschung [project VIPR 05D23VT1 (ERUM-DATA)]. We thank the Cluster of Excellence– Machine Learning for Science, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (EXC No. 2064/1, project No. 390727645), for support and access to computer resources. We also thank the DFG [grant No. 460248799 to DAPHNE4NFDI, HI1927/7-1 (project 557510229) and SCHR700/50-1]. The authors acknowledge the OSCARS project, which has received funding from the European Commission’s Horizon Europe Research and Innovation programme under grant agreement No. 101129751. Oliver Anyanor thanks the UK Engineering and Physical Sciences Research Council for funding his PhD studentship (grant No. EP/W524694/1). |
| Issue: |
3 |