Cordova, ManuelMoutzouri, Pinelopide Almeida, Bruno SimoesTorodii, DariaEmsley, Lyndon2023-02-132023-02-132023-02-132023-01-1310.1002/anie.202216607https://infoscience.epfl.ch/handle/20.500.14299/194754WOS:000912999600001The resolution of proton solid-state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic-angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to completely remove dipolar interactions. Here, we introduce a deep learning approach to determine pure isotropic proton spectra from a two-dimensional set of magic-angle spinning spectra acquired at different spinning rates. Applying the model to 8 organic solids yields high-resolution H-1 solid-state NMR spectra with isotropic linewidths in the 50-400 Hz range.Chemistry, MultidisciplinaryChemistrymachine learningnmr spectroscopysolid-state structurescarbon-carbon connectivitiesstate nmrspectroscopysystemstext::journal::journal article::research article