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

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.

Automated summary

This paragraph discusses the status, opportunities, challenges, and limitations of machine learning applied to X-ray and neutron scattering techniques, focusing on surface scattering. Typical strategies and potential pitfalls are outlined. The applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for machine learning applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.