Journal
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
Volume 59, Issue 26, Pages 10297-10300Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/anie.201908162
Keywords
artificial intelligence; deep learning; fast sampling; NMR spectroscopy
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61571380, 61971361, 61871341, U1632274]
- Joint NSFC-Swedish Foundation for International Cooperation in Research and Higher Education (STINT) [61811530021]
- National Key R&D Program of China [2017YFC0108703]
- Natural Science Foundation of Fujian Province of China [2018J06018]
- Fundamental Research Funds for the Central Universities [20720180056]
- Science and Technology Program of Xiamen [3502Z20183053]
- China Scholarship Council [201806315010, 201808350010]
- Swedish Research Council [2015-04614]
- Swedish Foundation for Strategic Research [ITM17-0218]
- Xiamen University Nanqiang Outstanding Talents Program
- Swedish Research Council [2015-04614] Funding Source: Swedish Research Council
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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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