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Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Physics

Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

X. Kong, L. Zhou, et al.

Discover how a revolutionary method combining deep learning and sparse matrix completion enhances nanoscale NMR spectroscopy. This research, conducted by Xi Kong and colleagues, not only improves signal sensitivity by an impressive 5.7 dB but also tackles the challenges of data acquisition in molecular structure determination.

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Playback language: English
Abstract
Two-dimensional nuclear magnetic resonance (NMR) is crucial for molecule structure determination. Nitrogen-vacancy centers in diamond serve as quantum sensors for nanoscale NMR, but efficient data acquisition and processing are needed. This paper presents a method combining deep learning and sparse matrix completion to accelerate 2D nanoscale NMR spectroscopy. An artificial intelligence protocol enhances the signal-to-noise ratio by 5.7 ± 1.3 dB with 10% sampling coverage on a single nuclear spin cluster, improving sensitivity by intrinsically suppressing noise.
Publisher
npj Quantum Information
Published On
Sep 16, 2020
Authors
Xi Kong, Leixin Zhou, Zhijie Li, Zhiping Yang, Bensheng Qiu, Xiaodong Wu, Fazhan Shi, Jiangfeng Du
Tags
nanoscale NMR
deep learning
signal-to-noise ratio
quantum sensors
data acquisition
sparse matrix completion
molecule structure

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