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Related Experiment Video

Updated: May 7, 2025

Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor
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Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments.

Junqi Yin1, Viktor Reshniak2, Siyan Liu3

  • 1National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.

Structural Dynamics (Melville, N.Y.)
|December 30, 2024
PubMed
Summary

We developed an AI-driven computational framework for real-time steering of neutron scattering experiments. This approach significantly reduces data processing time and enhances experimental accuracy for materials research.

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Area of Science:

  • Materials Science
  • Computational Physics
  • Data Science

Background:

  • Neutron scattering experiments generate vast amounts of complex data.
  • Real-time analysis and adaptive control are crucial for optimizing experimental efficiency and accuracy.
  • Current data processing methods can be time-consuming, limiting experimental throughput.

Purpose of the Study:

  • To introduce a novel computational framework for real-time steering of neutron scattering experiments.
  • To leverage artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) for enhanced experimental control.
  • To reduce data processing bottlenecks and improve the accuracy of multidimensional crystallography.

Main Methods:

  • Integration of edge and exascale computing for an edge-to-exascale workflow.
  • Application of the Temporal Fusion Transformer model for predicting 3D neutron scattering patterns with voxel-level precision.
  • Temporal processing of four-dimensional neutron event data from the Spallation Neutron Source.

Main Results:

  • Demonstrated substantial reduction in data processing time from hours to minutes using distributed training.
  • Achieved significant improvements in model accuracy for predicting neutron scattering patterns.
  • Enabled adaptive, data-driven decisions during experiments through edge and exascale computing.

Conclusions:

  • The developed framework optimizes neutron beam time and improves experimental accuracy.
  • This approach lays the foundation for automation in neutron scattering experiments.
  • The system shows significant potential for widespread adoption and efficient exploration of complex material systems.