Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
This addresses the time-consuming nature of NMR spectroscopy for chemists and biologists, though it is a proof-of-concept and likely incremental.
The paper tackled the problem of long experimental times in nuclear magnetic resonance (NMR) spectroscopy by using deep learning to reconstruct high-quality spectra from limited data, achieving very fast reconstruction without needing large volumes of realistic training data.
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. 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. We show that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for a large volume of realistic training data usually required in the deep learning approach.