Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. In this work, we present a proof-of-concept of application of deep learning and neural network for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. Experimental results show that the neural network training can be achieved using solely synthetic NMR signal with exponential functions, which lifts the prohibiting demand for a large volume of realistic training data usually required in the deep learning approach.
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