Machine learning for scattering data: strategies, perspectives and applications to surface scattering
Greco, Alessandro
Starostin, Vladimir
Munteanu, Valentin
Pithan, Linus
Gerlach, Alexander
Schreiber, Frank
DOI: https://doi.org/10.1107/S1600576722011566
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/11419
Starostin, Vladimir; 1University of TübingenInstitute of Applied PhysicsAuf der Morgenstelle 10 Tübingen 72076 Germany
Munteanu, Valentin; 1University of TübingenInstitute of Applied PhysicsAuf der Morgenstelle 10 Tübingen 72076 Germany
Pithan, Linus; 1University of TübingenInstitute of Applied PhysicsAuf der Morgenstelle 10 Tübingen 72076 Germany
Gerlach, Alexander; 1University of TübingenInstitute of Applied PhysicsAuf der Morgenstelle 10 Tübingen 72076 Germany
Schreiber, Frank; 1University of TübingenInstitute of Applied PhysicsAuf der Morgenstelle 10 Tübingen 72076 Germany
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.
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.