Pure isotropic proton NMR spectra in solids using deep learning
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{
"updated": "2023-03-10T13:50:42.037468+00:00",
"id": "1595",
"metadata": {
"id": "1595",
"status": "published",
"_files": [
{
"description": "Python code used to train and use the model and pre-trained model",
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"key": "code.zip",
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},
{
"description": "Datasets of variable-rate MAS 1H NMR experiments on which the model is applied",
"size": 13653317,
"key": "MAS_datasets.zip",
"checksum": "md5:1cf1c48691243aa4f1570313a26b8fab"
},
{
"description": "NMR experiments used to determine the assignment of the proton spectra",
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"checksum": "md5:ec2e06aef2effca4210ca36cab4c4889"
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{
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{
"description": "Quantum ESPRESSO DFT chemical shift computations to assign chemical shifts of MDMA hydrochloride",
"size": 48800,
"key": "Quantum_espresso.zip",
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],
"contributors": [
{
"givennames": "Manuel",
"familyname": "Cordova",
"affiliations": [
"Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
],
"email": "manuel.cordova@epfl.ch"
},
{
"givennames": "Pinelopi",
"familyname": "Moutzouri",
"affiliations": [
"Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
]
},
{
"givennames": "Bruno",
"familyname": "Sim\u00f5es de Almeida",
"affiliations": [
"Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
]
},
{
"givennames": "Daria",
"familyname": "Torodii",
"affiliations": [
"Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
]
},
{
"givennames": "Lyndon",
"familyname": "Emsley",
"affiliations": [
"Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
],
"email": "lyndon.emsley@epfl.ch"
}
],
"conceptrecid": "1594",
"doi": "10.24435/materialscloud:a7-59",
"references": [
{
"url": "https://doi.org/10.1002/anie.202216607",
"citation": "M. Cordova, P. Moutzouri, B. Sim\u00f5es de Almeida, D. Torodii, L. Emsley, Angew. Chem. Int. Ed. 62, e202216607 (2023)",
"comment": "Paper in which the method is described",
"type": "Journal reference",
"doi": "10.1002/anie.202216607"
},
{
"url": "https://github.com/manucordova/PIPNet",
"citation": "M. Cordova, P. Moutzouri, B. Sim\u00f5es de Almeida, D. Torodii, L. Emsley, Angew. Chem. Int. Ed. 62, e202216607 (2023)",
"comment": "Github repository containing the code",
"type": "Software",
"doi": ""
}
],
"title": "Pure isotropic proton NMR spectra in solids using deep learning",
"publication_date": "Dec 22, 2022, 10:15:47",
"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.",
"mcid": "2022.180",
"edited_by": 339,
"version": 1,
"is_last": true,
"owner": 339,
"license_addendum": null,
"keywords": [
"MARVEL/DD1",
"machine learning",
"NMR",
"resolution"
],
"_oai": {
"id": "oai:materialscloud.org:1595"
},
"license": "Creative Commons Attribution Share Alike 4.0 International"
},
"revision": 6,
"created": "2022-12-21T14:48:53.537570+00:00"
}