Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning. Issue 26 (15th April 2020) - Qu, Xiaobo; Huang, Yihui; Lu, Hengfa; Qiu, Tianyu; Guo, Di; Agback, Tatiana; Orekhov, Vladislav; Chen, Zhong
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. Abstract : A proof of concept is presented for the application of deep learning, an artificial intelligence technique, for high‐quality, reliable, and very fast NMR spectra reconstruction from limited experimental data.