Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 519-525 |
| Number of pages | 7 |
| Journal | Journal of Applied Crystallography |
| Volume | 56 |
| Issue number | Pt 2 |
| DOIs | |
| State | Published - Apr 1 2023 |
Funding
The research at Oak Ridge National Laboratory (ORNL) was supported by the US Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research (contract No. ERKJ387); and the Office of Basic Energy Sciences, Early Career Research Program (award No. KC0402020; contract No. DE-AC05-00OR22725). This research used resources at the High Flux Isotope Reactor, a DOE Office of Science User Facility operated by ORNL.
Keywords
- data reduction
- machine learning
- neutron diffraction
- peak recognition
- uncertainty quantification