Machine learning for neutron scattering at ORNL*. Issue 2 (29th December 2020)

• Record Type: Journal Article
• Authors: Doucet, Mathieu; Samarakoon, Anjana M; Do, Changwoo; Heller, William T; Archibald, Richard; Alan Tennant, D; Proffen, Thomas; Granroth, Garrett E
• Is Part Of: Machine learning: science and technology. Volume 2:Issue 2(2021)
• Publication Date: 2020-12-29
• Subjects: neutron scattering -- machine learning -- spectroscopy -- diffraction -- sans -- super resolution; 006.31
• DOI: 10.1088/2632-2153/abcf88
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: Machine learning (ML) offers exciting new opportunities to extract more information from scattering data. At neutron scattering user facilities, ML has the potential to help accelerate scientific productivity by empowering facility users with insight into their data which has traditionally been supplied by scattering experts. Such support can help in both speeding up common modeling problems for users, as well as help solve harder problems that are normally time consuming and difficult to address with standard methods. This article explores the recent ML work undertaken at Oak Ridge National Laboratory involving neutron scattering data. We cover materials structure modeling for diffuse scattering, powder diffraction, and small-angle scattering. We also discuss how ML can help to model the response of the instrument more precisely, as well as enable quick extraction of information from neutron data. The application of super-resolution techniques to small-angle scattering and peak extraction for diffraction will be discussed.