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.

Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments

“…involving electrochemical control over battery charging or control over electrochemical sample environment conditions or catalytic conditions in experiments on nanoparticles. It also extends to other synchrotron-based techniques (Chen et al, 2021;Hinderhofer et al, 2023), as well as neutron-based techniques, including in particular neutron reflectometry (Treece et al, 2019).…”

“…Consequently, it is vital in reflectivity analysis to make use of the physical understanding of the investigated system in order to reduce the number of potential solutions and identify the correct one. In previous ML-based works with two-layered structures (Greco et al, 2019;Hinderhofer et al, 2023), we approached this task by effectively fixing most of the parameters characterizing the SLD profile and training the neural network to estimate only the three unfixed parameters -thickness, roughness and density of the top organic layer -anticipated to vary among the samples under study, with parameters of the silicon substrate with a silica top layer held constant. Expanding this method to accommodate a larger set of variable parameters, further techniques to address the ambiguity problem are needed (Munteanu et al, 2023).…”

Multi-task scattering-model classification and parameter regression of nanostructures from small-angle scattering data

“…Recently, there have been many studies that apply ML to 1-D Xray and neutron scattering data to predict structural parameters and properties of a variety of organic and inorganic structures. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] We briey highlight the few that are most relevant to our work. Franke et al developed a ML technique to analyze SAXS data, categorizing biomolecules into distinct shapes and estimating their structural parameters.…”

INSIGHT: in situ heuristic tool for the efficient reduction of grazing-incidence X-ray scattering data

“…This extensive software package simulates and fits reflectometry, GISAXS and GISANS data, and is often used for neutron scattering data and advanced multilayer modeling of 2D detector data. Recent publications have also focused on machine learning in connection with grazing-incidence scattering (Van Herck et al, 2021;Hinderhofer et al, 2023). The package pyFAI performs azimuthal integration on transmission SAXS and WAXS data (Ashiotis et al, 2015).…”