ShimNetV2‐RM Neural Network for Post‐Acquisition Correction of Spectral Lineshapes in NMR Reaction Monitoring

NMR can be used to monitor reactions in situ, typically by acquiring a series of standard one-dimensional spectra at regular intervals. However, the experimental setup is more demanding than for stable, nonreacting samples. For example, the usual pre-acquisition correction of magnetic field homogeneity, known as shimming, can sometimes be too time-consuming to be repeated before each spectrum in a series. This results in a gradual increase in spectral lineshape distortions during the reaction. Fortunately, the spectra can be improved to some extent by applying post-acquisition corrections. We propose replacing the standard semi-manual reference deconvolution method with a neural network called ShimNetV2-RM. Our method, previously developed under the name ShimNet for standard nonreacting samples, can be tailored for reaction monitoring by constraining training parameters using well-shimmed pre- and post-monitoring spectra. To demonstrate the applicability of the proposed approach under realistic and demanding conditions, we applied ShimNetV2-RM to two representative copper-catalyzed azide-alkyne cycloaddition (CuAAC) reactions.