Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Xi Kong; Leixin Zhou; Zhijie Li; Zhiping Yang; Bensheng Qiu; Xiaodong Wu; Fazhan Shi; Jiangfeng Du

doi:10.1038/s41534-020-00311-z

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Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Journal article

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Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Xi Kong, Leixin Zhou, Zhijie Li, Zhiping Yang, Bensheng Qiu, Xiaodong Wu, Fazhan Shi and Jiangfeng Du

npj quantum information, Vol.6(1), 79

09/16/2020

DOI: 10.1038/s41534-020-00311-z

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Abstract

AbstractTwo-dimensional nuclear magnetic resonance (NMR) is indispensable to molecule structure determination. Nitrogen-vacancy center in diamond has been proposed and developed as an outstanding quantum sensor to realize NMR in nanoscale or even single molecule. However, like conventional multi-dimensional NMR, a more efficient data accumulation and processing method is necessary to realize applicable two-dimensional (2D) nanoscale NMR with a high spatial resolution nitrogen-vacancy sensor. Deep learning is an artificial algorithm, which mimics the network of neurons of human brain, has been demonstrated superb capability in pattern identifying and noise canceling. Here we report a method, combining deep learning and sparse matrix completion, to speed up 2D nanoscale NMR spectroscopy. The signal-to-noise ratio is enhanced by 5.7 ± 1.3 dB in 10% sampling coverage by an artificial intelligence protocol on 2D nanoscale NMR of a single nuclear spin cluster. The artificial intelligence algorithm enhanced 2D nanoscale NMR protocol intrinsically suppresses the observation noise and thus improves sensitivity.

Details

Title: Subtitle: Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy
Creators: Xi Kong - Nanjing University
Leixin Zhou - University of Iowa
Zhijie Li - University of Science and Technology of China
Zhiping Yang - University of Science and Technology of China
Bensheng Qiu - University of Science and Technology of China
Xiaodong Wu - University of Iowa
Fazhan Shi - University of Science and Technology of China
Jiangfeng Du - University of Science and Technology of China
Resource Type: Journal article
Publication Details: npj quantum information, Vol.6(1), 79
DOI: 10.1038/s41534-020-00311-z
ISSN: 2056-6387
eISSN: 2056-6387
Grant note: DOI: 10.13039/501100002855, name: Ministry of Science and Technology of the People’s Republic of China
Language: English
Date published: 09/16/2020
Academic Unit: Electrical and Computer Engineering; Radiation Oncology; The Iowa Institute for Biomedical Imaging
Record Identifier: 9984197266102771

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