Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning |
| |
| Authors: | Prof Xiaobo Qu Yihui Huang Hengfa Lu Tianyu Qiu Prof Di Guo Dr Tatiana Agback Prof Vladislav Orekhov Prof Zhong Chen |
| |
| Institution: | 1. 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;2. School of Computer and Information Engineering, Xiamen University of Technology, China;3. Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden;4. Department of Chemistry and Molecular Biology, University of Gothenburg, Box 465, Gothenburg, 40530 Sweden |
| |
| 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. |
| |
| Keywords: | artificial intelligence deep learning fast sampling NMR spectroscopy |
|
|
