Physics-informed deep learning enables fast ultrahigh-resolution nuclear magnetic resonance spectroscopy.

ABSTRACT: Pure shift NMR spectroscopy has found extensive applications in exploring the structure, function, and interactions of molecules in an ultrahigh-resolution manner. However, time-consuming data acquisition resulting from additional time dimension for pure shift evolution impedes its further applications. In this study, a general and robust AI-assisted NMR methodology combining non-uniform chunk sampling with physics-informed deep learning (DL) reconstruction is proposed for fast implementation of pure shift NMR spectroscopy. The proposed DL protocol enables the suppression on sparsely sampling artifacts, faithful recovery of weak signals, as well as high-fidelity reconstruction on peak intensities, thus implementing accelerated pure shift NMR while maintaining spectral quality. The well-tra

SUBMITTER: Bao J 

PROVIDER: S-EPMC12932822 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature