Machine-learning-assisted automation of single-crystal neutron diffraction Article Swipe
Yiqing Hao
,
Erxi Feng
,
Dan Lu
,
Leah Zimmer
,
Zachary Morgan
,
Bryan C. Chakoumakos
,
Guannan Zhang
,
Huibo Cao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1107/s1600576723001516
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1107/s1600576723001516
Neutron scattering is a powerful but expensive technique to study materials and discover new matter. Advanced detector technology has significantly improved the efficiency of neutron experiments, increasing the complexity of neutron data reduction and analysis. Machine learning (ML) brings new directions for neutron diffraction data reduction and experiment operation. This work presents an ML-assisted data reduction and analysis method for precise recognition of Bragg peaks and the corresponding regions of interest; it can then automatically screen and align a measured crystal using the recognized peaks, and subsequently plan and optimize the data collection with user-provided information and uncertainty quantification values of detected peaks. This method shows robust performance in different complex sample environments and enables automated single-crystal neutron diffraction.
Related Topics
Concepts
Neutron diffraction
Neutron
Automation
Neutron scattering
Detector
Data reduction
Reduction (mathematics)
Computer science
Diffraction
Materials science
Sample (material)
Crystal (programming language)
Single crystal
Neutron detection
Physics
Optics
Nuclear physics
Data mining
Nuclear magnetic resonance
Mathematics
Engineering
Mechanical engineering
Programming language
Geometry
Thermodynamics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1107/s1600576723001516
- OA Status
- green
- Cited By
- 9
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362591849
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362591849Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1107/s1600576723001516Digital Object Identifier
- Title
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Machine-learning-assisted automation of single-crystal neutron diffractionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-04-01Full publication date if available
- Authors
-
Yiqing Hao, Erxi Feng, Dan Lu, Leah Zimmer, Zachary Morgan, Bryan C. Chakoumakos, Guannan Zhang, Huibo CaoList of authors in order
- Landing page
-
https://doi.org/10.1107/s1600576723001516Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.osti.gov/biblio/1996707Direct OA link when available
- Concepts
-
Neutron diffraction, Neutron, Automation, Neutron scattering, Detector, Data reduction, Reduction (mathematics), Computer science, Diffraction, Materials science, Sample (material), Crystal (programming language), Single crystal, Neutron detection, Physics, Optics, Nuclear physics, Data mining, Nuclear magnetic resonance, Mathematics, Engineering, Mechanical engineering, Programming language, Geometry, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 7, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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