Machine learning for scattering data: strategies, perspectives and applications to 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.

SUBMITTER: Hinderhofer A 

PROVIDER: S-EPMC9901926 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature