Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments
This work addresses the need for real-time decision-making in X-ray scattering experiments, particularly for researchers at synchrotron facilities, though it appears incremental as it builds on existing ML applications in this domain.
The study tackled the problem of enabling autonomous experiments in synchrotron beamline environments by integrating machine learning for online data analysis in X-ray reflectometry, resulting in a closed-loop feedback system that demonstrated accuracy and robustness in controlling a vacuum deposition setup for organic thin film growth.
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.