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Pure Isotropic Proton NMR Spectra in Solids using Deep Learning |
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| Authors: | Manuel Cordova Pinelopi Moutzouri Bruno Simões de Almeida Daria Torodii Prof Lyndon Emsley |
| Institution: | 1. Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
National Centre for Computational Design and Discovery of Novel Materials MARVEL, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland;2. Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland |
| Abstract: | 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. |
| Keywords: | Machine Learning NMR Spectroscopy Solid-State Structures |