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

arXiv: https://arxiv.org/abs/2306.11899 · HTML: https://mlsanity.com/paper/2306.11899/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Built on by (most cited first, 7 of 7)

- [Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation](https://mlsanity.com/paper/2407.18648/) (2024)
- [NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression](https://mlsanity.com/paper/2409.05785/) (2024)
- [Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem](https://mlsanity.com/paper/2307.05364/) (2023)
- [Autonomous battery research: Principles of heuristic operando experimentation](https://mlsanity.com/paper/2601.00851/) (2025)
- [Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders](https://mlsanity.com/paper/2606.14999/) (2026)
- [Advancing from Automated to Autonomous Beamline by Leveraging Computer Vision](https://mlsanity.com/paper/2506.00836/) (2025)
- [Towards real-time surrogate-free Bayesian inversion for neutron reflectometry](https://mlsanity.com/paper/2509.06924/) (2025)

## Abstract (arXiv)

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.