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Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Author(s) -
Qu Xiaobo,
Huang Yihui,
Lu Hengfa,
Qiu Tianyu,
Guo Di,
Agback Tatiana,
Orekhov Vladislav,
Chen Zhong
Publication year - 2020
Publication title -
angewandte chemie
Language(s) - English
Resource type - Journals
eISSN - 1521-3757
pISSN - 0044-8249
DOI - 10.1002/ange.201908162
Subject(s) - nuclear magnetic resonance spectroscopy , deep learning , spectroscopy , artificial neural network , nuclear magnetic resonance , computer science , artificial intelligence , materials science , chemistry , physics , quantum mechanics
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.