2026-07-22T17:58:51Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:1595 2022-12-22T10:15:47Z community-mcarchive openaire_data

Cordova, Manuel Emsley, Lyndon Cordova, Manuel Moutzouri, Pinelopi Simões de Almeida, Bruno Torodii, Daria Emsley, Lyndon 2022-12-22 The 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 1H solid-state NMR spectra with isotropic linewidths in the 50-400 Hz range. application/zip text/markdown application/zip application/zip application/zip application/zip https://doi.org/10.24435/materialscloud:a7-59 oai:materialscloud.org:1595 mcid:2022.180 eng Materials Cloud https://doi.org/10.1002/anie.202216607 https://github.com/manucordova/PIPNet https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:jk-zx info:eu-repo/semantics/openAccess Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode MARVEL/DD1 machine learning NMR resolution Pure isotropic proton NMR spectra in solids using deep learning info:eu-repo/semantics/other