ArticleJournal of synchrotron radiation2023
Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments.
Article in Journal of synchrotron radiation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Toward Unified Autonomous Scattering Experiments: A Cross-Facility Case Study at ALS and PETRA III.Photon science · 2026Article
- From Static Structures to Molecular Dynamics: Emerging Directions in X-Ray and Electron Materials Characterization.Materials (Basel, Switzerland) · 2026Review
- Robust Automatic EXAFS First-Shell Fits.Photon science · 2026Article
- Synchrotron beamline setup enabling quasi-simultaneous PXRD and XANES measurements: case study of Fischer-Tropsch catalyst reduction at 60 bar.Journal of synchrotron radiation · 2026Article
- Towards machine-learning-based on-the-fly analysis of neutron reflectometry.Journal of applied crystallography · 2026Article
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- Modular Adaptive Processing Infrastructure (MAPI): a blueprint for interconnecting generic workflows with modern interfaces.Journal of synchrotron radiation · 2026Article
- Data selection strategies for minimizing measurement time in materials characterization.Scientific reports · 2025Article
- Benchmarking deep learning for automated peak detection on GIWAXS data.Journal of applied crystallography · 2025Article
- Fast and reliable probabilistic reflectometry inversion with prior-amortized neural posterior estimation.Science advances · 2025Article
- Millisecond X-ray reflectometry and neural network analysis: unveiling fast processes in spin coating.Journal of applied crystallography · 2024Article
- Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge.Journal of applied crystallography · 2024Article
- Deep learning at the edge enables real-time streaming ptychographic imaging.Nature communications · 2023Article
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Authors and funding
13 authors.
Funding
Abstract
Recently, there has been significant interest in applying machine-learning (ML) techniques to the automated analysis of X-ray scattering experiments, due to the increasing speed and size at which datasets are generated. ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described. ML-based online analysis results are processed in 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. Our data demonstrate 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.