<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Cordova, Manuel</dc:creator> <dc:creator>Moutzouri, Pinelopi</dc:creator> <dc:creator>Simões de Almeida, Bruno</dc:creator> <dc:creator>Torodii, Daria</dc:creator> <dc:creator>Emsley, Lyndon</dc:creator> <dc:date>2022-12-22</dc:date> <dc:description>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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2022.180</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:a7-59</dc:identifier> <dc:identifier>mcid:2022.180</dc:identifier> <dc:identifier>oai:materialscloud.org:1595</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode</dc:rights> <dc:subject>MARVEL/DD1</dc:subject> <dc:subject>machine learning</dc:subject> <dc:subject>NMR</dc:subject> <dc:subject>resolution</dc:subject> <dc:title>Pure isotropic proton NMR spectra in solids using deep learning</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>