Speaker
Description
Surface and interface scattering is an indispensable tool in modern thin film research and material science. X-ray and neutron reflectivity measurements offer a diverse array of experimental insights, presenting an opportunity to integrate this technique through machine learning (ML) / Neural Network-based methods. Within the framework of DAPHNE4NFDI (Data for Photon and Neutron Science), collaborative efforts between the groups at the University of Kiel (Dr. Bridget Murphy) and Tübingen (Prof. Frank Schreiber) are focused on enhancing machine learning models for the analysis of X-ray and Neutron reflectivity datasets by the Python package "mlreflect". [1]
After introducing general concepts of ML, we will discuss the opportunities and challenges related to ML for the analysis of surface scattering data. In particular, we will discuss the specific difficulties related to real-world surface-scattering experiments, including background scattering, small, but finite miscalibration of the incident angle, as well as the specifics of surface scattering using neutrons with peculiar cases of the scattering length density (SLD) profile, which can lead to the absence of a critical angle. [1,2]
We will discuss an open experimental dataset of raw X-ray reflectivity measurements together with corresponding fit parameters, intentionally published to use as training or test data for machine learning models. [3,4]
Finally, we will briefly comment on the opportunities of closed-loop experiments [5] using ML for quasi-real-time data analysis and direct feedback to the experiment.
We acknowledge financial support by the BMBF and the DFG (ErUM-data, ErUM-pro, and DAPHNE4NFDI).
[1] Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental and feature engineering, A. Greco et al., Jounal of Applied Crystallography, 55, 362 (2022)
[2]A. Greco, V. Starostin, A. Hinderhofer, A. Gerlach, M. W. A. Skoda, S. Kowarik, and F. Schreiber.
Neural network analysis of neutron and X-ray reflectivity data: Pathological cases, performance and perspectives Mach. Learn.: Sci. Technol. 2 (2021) 045003
[3] Reflectometry curves (XRR and NR) and corresponding fits for machine learning. Pithan, Linus, Greco, Alessandro, Hinderhofer, Alexander, Gerlach, Alexander, Kowarik, Stefan, Rußegger, Nadine, Dax, Ingrid, & Schreiber, Frank. (2022). Zenodo. https://doi.org/10.5281/zenodo.6497438
[4] A. Hinderhofer, A. Greco, V. Starostin, V. Munteanu, L. Pithan, A. Gerlach, and F. Schreiber.
Machine learning for scattering data: strategies, perspectives, and applications to surface scattering
J. Appl. Cryst. 56 (2023) 3
[5] Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments, L. Pithan et al., J. Synchrotron Rad., in print (2023)
Please note that this talk will be recorded.
What topics do you think we should discuss in the working sessions?
Metadata schema standards for open data
| Which point of view is your contribution addressing? | I've made open datasets available for machine learning training purposes |
|---|---|
| What best describes your position? | machine learning expert |
