TY  - JOUR
A1  - Hinderhofer, Alexander
A1  - Greco, Alessandro
A1  - Starostin, Vladimir
A1  - Munteanu, Valentin
A1  - Pithan, Linus
A1  - Gerlach, Alexander
A1  - Schreiber, Frank
T1  - Machine learning for scattering data: strategies, perspectives and applications to surface scattering
Y1  - 2023-01-24
VL  - 56
IS  - 1
SP  - 3
EP  - 11
JF  - Journal of Applied Crystallography
DO  - 10.1107/S1600576722011566
PB  - International Union of Crystallography
N2  - 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.
N2  - 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.
UR  - http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/11419
ER  -