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Writing Bragg Gratings in Multicore Fibers
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BraggNet: integrating Bragg peaks using neural networks.

Brendan Sullivan1, Rick Archibald2, Jahaun Azadmanesh3,4

  • 1Neutron Scattering Division, Neutron Sciences Directorate, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA.

Journal of Applied Crystallography
|August 10, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning, specifically neural networks, can now accurately refine peak shapes and integrate data in neutron crystallography. This advancement promises to improve the quality of crystallographic data for various techniques.

Keywords:
computational modellingintegrationmachine learningneural networksneutron crystallography

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Area of Science:

  • Crystallography
  • Machine Learning
  • Structural Biology

Background:

  • Neutron crystallography is valuable for determining the positions of light atoms like hydrogen, complementing X-ray crystallography.
  • Macromolecular neutron crystallography is hindered by challenges in integrating pulsed-source experimental data, particularly peak shapes.

Purpose of the Study:

  • To develop and demonstrate the application of machine learning for refining peak shapes and improving data integration in neutron crystallography.
  • To advance existing software for neutron crystallography data analysis.

Main Methods:

  • An artificial neural network, based on the U-Net architecture, was trained using simulated neutron crystallography peaks.
  • The neural network was trained over approximately 100,000 simulated peaks for 100 epochs.
  • Performance was evaluated using the Dice coefficient and compared against negative control data sets.

Main Results:

  • The neural network achieved a Dice coefficient of approximately 65% for peak shape prediction, significantly outperforming control data sets (15%).
  • Integration of entire peak sets using the neural network resulted in improved intensity statistics compared to methods like k-nearest neighbors.
  • Demonstrated the capability of neural networks to learn peak shapes and perform Bragg peak integration.

Conclusions:

  • Neural network-based integration represents a significant advancement for neutron crystallography data processing.
  • This machine learning approach is expected to enhance the quality of data obtained from neutron, electron, and X-ray crystallography.
  • The findings pave the way for broader adoption of machine learning in crystallographic data analysis.