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Two-dimensional pure isotropic proton solid state NMR

Pinelopi Moutzouri1, Manuel Cordova1*, Bruno Simões de Almeida1, Daria Torodii1, Lyndon Emsley1*

1 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

* Corresponding authors emails: manuel.cordova@epfl.ch, lyndon.emsley@epfl.ch
DOI10.24435/materialscloud:xj-5f [version v1]

Publication date: Mar 10, 2023

How to cite this record

Pinelopi Moutzouri, Manuel Cordova, Bruno Simões de Almeida, Daria Torodii, Lyndon Emsley, Two-dimensional pure isotropic proton solid state NMR, Materials Cloud Archive 2023.41 (2023), doi: 10.24435/materialscloud:xj-5f.


One key bottleneck of solid-state NMR spectroscopy is that ¹H NMR spectra of organic solids are often very broad due to the presence of a strong network of dipolar couplings. We have recently suggested a new approach to tackle this problem. More specifically, we parametrically mapped errors leading to residual dipolar broadening into a second dimension and removed them in a correlation experiment. In this way pure isotropic proton (PIP) spectra were obtained that contain only isotropic shifts and provide the highest ¹H NMR resolution available today in rigid solids. Here, using a deep-learning method, we extend the PIP approach to a second dimension, and for samples of L-tyrosine hydrochloride and ampicillin we obtain high resolution ¹H-¹H double-quantum/single-quantum dipolar correlation and spin-diffusion spectra with significantly higher resolution than the corresponding spectra at 100 kHz MAS, allowing the identification of previously overlapped isotropic correlation peaks.

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File name Size Description
48.3 MiB Python code used to train and use the model and pre-trained model
4.0 GiB Experimental datasets on which the model is applied


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.


MARVEL/DD1 machine learning NMR resolution

Version history:

2023.41 (version v1) [This version] Mar 10, 2023 DOI10.24435/materialscloud:xj-5f