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

• URL: http://arxiv.org/abs/2306.11899v1
• Date: Tue, 20 Jun 2023 21:21:19 GMT
• Title: Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments
• Authors: Linus Pithan (1), Vladimir Starostin (1), David Mare\v{c}ek (2), Lukas Petersdorf (3), Constantin V\"olter (1), Valentin Munteanu (1), Maciej Jankowski (4), Oleg Konovalov (4), Alexander Gerlach (1), Alexander Hinderhofer (1), Bridget Murphy (3), Stefan Kowarik (2), Frank Schreiber (1) ((1) Universit\"at T\"ubingen Germany, (2) Universit\"at Graz Austria, (3) Universit\"at Kiel Germany, (4) ESRF France)
• Abstract summary: Machine learning can be used to enhance research involving large or rapidly generated datasets. In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR) We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment.
• Score: 80.49514665620008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, there has been significant interest in applying machine learning (ML) techniques to X-ray scattering experiments, which proves to be a valuable tool for enhancing research that involves large or rapidly generated datasets. ML allows for the automated interpretation of experimental results, particularly those obtained from synchrotron or neutron facilities. The speed at which ML models can process data presents an important opportunity to establish a closed-loop feedback system, enabling real-time decision-making based on online data analysis. In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR), using the growth of organic thin films as an example. Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback. We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment. Our data demonstrates the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup.

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