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Recently, there has been significant interest in applying machine learning(ML) techniques to X-ray scattering experiments, which proves to be a valuabletool 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 speedat which ML models can process data presents an important opportunity toestablish a closed-loop feedback system, enabling real-time decision-makingbased on online data analysis. In this study, we describe the incorporation ofML into a closed-loop workflow for X-ray reflectometry (XRR), using the growthof organic thin films as an example. Our focus lies on the beamline integrationof ML-based online data analysis and closed-loop feedback. We present solutionsthat provide an elementary data analysis in real time during the experimentwithout introducing the additional software dependencies in the beamlinecontrol software environment. Our data demonstrates the accuracy and robustnessof ML methods for analyzing XRR curves and Bragg reflections and its autonomouscontrol over a vacuum deposition setup.

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