Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning†
Corresponding Author
Prof. Xiaobo Qu
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorYihui Huang
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorHengfa Lu
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorTianyu Qiu
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorProf. Di Guo
School of Computer and Information Engineering, Xiamen University of Technology, China
Search for more papers by this authorDr. Tatiana Agback
Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
Search for more papers by this authorProf. Vladislav Orekhov
Department of Chemistry and Molecular Biology, University of Gothenburg, Box 465, Gothenburg, 40530 Sweden
Search for more papers by this authorCorresponding Author
Prof. Zhong Chen
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorCorresponding Author
Prof. Xiaobo Qu
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorYihui Huang
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorHengfa Lu
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorTianyu Qiu
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorProf. Di Guo
School of Computer and Information Engineering, Xiamen University of Technology, China
Search for more papers by this authorDr. Tatiana Agback
Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
Search for more papers by this authorProf. Vladislav Orekhov
Department of Chemistry and Molecular Biology, University of Gothenburg, Box 465, Gothenburg, 40530 Sweden
Search for more papers by this authorCorresponding Author
Prof. Zhong Chen
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, P.O.Box 979, Xiamen, 361005 China
Search for more papers by this authorA previous version of this manuscript was deposited on a preprint server (https://arxiv.org/abs/1904.05168) on April 09, 2019.
Abstract
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof-of-concept of the application of deep learning and neural networks for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.
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