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Related Experiment Video

Updated: May 5, 2026

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
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DEEP Phaser: A Deep Learning Tandem Vision Transformer for Fully Automated NMR Phase Correction.

Da-Wei Li1, Lei Bruschweiler-Li1, Kyungsuh Lee1

  • 1Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States.

The Journal of Physical Chemistry Letters
|May 4, 2026
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Summary

A new deep learning method, DEEP Phaser, accurately corrects nuclear magnetic resonance (NMR) spectra phase. This automated approach eliminates the need for manual adjustments in NMR data processing.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Computational Chemistry

Background:

  • Phase correction is a critical step in Nuclear Magnetic Resonance (NMR) data processing.
  • Current automated methods often necessitate manual intervention by experts for optimal results.

Purpose of the Study:

  • To develop an advanced, automated phase correction algorithm for NMR spectra.
  • To achieve high phasing accuracy across diverse experimental NMR datasets without manual adjustments.

Main Methods:

  • Implementation of a deep learning algorithm utilizing a tandem vision transformer artificial neural network.
  • Training the model on a comprehensive dataset of synthetic solution-NMR spectra.
  • The algorithm determines zeroth- and first-order phase correction based on the entire spectrum.

Main Results:

  • DEEP Phaser demonstrates very high phasing accuracy on a wide array of experimental solution 1D NMR spectra.
  • The method is effective for various sample types, including small molecules, complex mixtures, and biomacromolecules.
  • No further manual adjustments were required for the tested spectra.

Conclusions:

  • DEEP Phaser offers a robust and automated solution for NMR spectra phase correction.
  • The deep learning approach significantly improves efficiency and accuracy in NMR data processing.
  • The software is publicly available as free software and a web server for broad accessibility.