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

• URL: http://arxiv.org/abs/2306.11899v1
• Date: Tue, 20 Jun 2023 21:21:19 GMT
• Title: Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments
• Authors: Linus Pithan (1), Vladimir Starostin (1), David Mare\v{c}ek (2), Lukas Petersdorf (3), Constantin V\"olter (1), Valentin Munteanu (1), Maciej Jankowski (4), Oleg Konovalov (4), Alexander Gerlach (1), Alexander Hinderhofer (1), Bridget Murphy (3), Stefan Kowarik (2), Frank Schreiber (1) ((1) Universit\"at T\"ubingen Germany, (2) Universit\"at Graz Austria, (3) Universit\"at Kiel Germany, (4) ESRF France)
• Abstract summary: Machine learning can be used to enhance research involving large or rapidly generated datasets. In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR) 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.
• Score: 80.49514665620008
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function [0.3441582801949978]
  We introduce a new DL architecture using state-of-the-art Differential Transformer encoder-decoder architecture, MFliNet. We demonstrate the model's performance through carefully designed, complex tissue-mimicking phantoms and preclinical in-vivo cancer xenograft experiments.
  arXiv  Detail & Related papers  (2024-11-25T20:03:41Z)
• In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD [51.04126395480625]
  Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
  arXiv  Detail & Related papers  (2023-06-22T14:07:54Z)
• Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics [54.172707311728885]
  We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED) Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
  arXiv  Detail & Related papers  (2023-06-03T06:19:20Z)
• AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources [2.682578132719034]
  We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography.
  arXiv  Detail & Related papers  (2023-04-09T19:11:04Z)
• Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images [2.5954872177280346]
  Machine learning could revolutionize materials research through autonomous data collection and processing. We employ a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator to augment simulated data with realistic spatial frequency information. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set.
  arXiv  Detail & Related papers  (2023-01-18T19:19:27Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• Explainable Predictive Modeling for Limited Spectral Data [0.0]
  We introduce applying recent explainable AI techniques to interpret the prediction outcomes of high-dimensional and limited spectral data. Due to instrument resolution limitations, pinpointing important regions of the spectroscopy data creates a pathway to optimize the data collection process. We specifically design three different scenarios to ensure that the evaluation of ML models is robust for the real-time practice.
  arXiv  Detail & Related papers  (2022-02-09T15:46:17Z)
• Machine learning enabling high-throughput and remote operations at large-scale user facilities [0.0]
  Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. We demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II)
  arXiv  Detail & Related papers  (2022-01-09T17:43:03Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.