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

TL;DR

This paper demonstrates how machine learning can be integrated into synchrotron beamline experiments to enable real-time data analysis and autonomous control, improving efficiency and decision-making during X-ray reflectometry studies.

Contribution

It introduces a novel workflow that incorporates ML-based online data analysis into beamline experiments, enabling closed-loop feedback without additional software dependencies.

Findings

ML provides accurate and robust analysis of XRR data.

The system enables autonomous control of a vacuum deposition setup.

Real-time analysis improves experimental efficiency.

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.