Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy
Dicheng Chen, Zi Wang, Di Guo, Vladislav Orekhov, Xiaobo Qu
Abstract
Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, and so forth. Herein, applications of DL in NMR spectroscopy are summarized, and a perspective for DL as an entirely new approach that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life sciences is outlined.
Topics & Concepts
Nuclear magnetic resonanceSpectroscopyNuclear magnetic resonance spectroscopyMaterials scienceChemistryPhysicsAstronomyNMR spectroscopy and applicationsAdvanced MRI Techniques and ApplicationsMetabolomics and Mass Spectrometry Studies