Machine learning for scattering data: strategies, perspectives and applications to surface scattering. Issue 1 (24th January 2023)

• Record Type: Journal Article
• Authors: Hinderhofer, Alexander; Greco, Alessandro; Starostin, Vladimir; Munteanu, Valentin; Pithan, Linus; Gerlach, Alexander; Schreiber, Frank
• Is Part Of: Journal of applied crystallography. Volume 56:Issue 1(2023)
• Page Start: 3
• Page End: 11
• Publication Date: 2023-01-24
• Subjects: surface scattering -- X‐ray diffraction -- neutron scattering -- machine learning -- data analysis; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576722011566
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : The status, opportunities, challenges and limitations of machine learning are discussed as applied to X‐ray and neutron scattering techniques, with an emphasis on surface scattering. Abstract : Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X‐ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing‐incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.