Pure isotropic proton NMR spectra in solids using deep learning

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{
  "id": "1595", 
  "updated": "2023-03-10T13:50:42.037468+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Manuel", 
        "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", 
        "familyname": "Cordova"
      }, 
      {
        "givennames": "Pinelopi", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Moutzouri"
      }, 
      {
        "givennames": "Bruno", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Sim\u00f5es de Almeida"
      }, 
      {
        "givennames": "Daria", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Torodii"
      }, 
      {
        "givennames": "Lyndon", 
        "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", 
        "familyname": "Emsley"
      }
    ], 
    "title": "Pure isotropic proton NMR spectra in solids using deep learning", 
    "_oai": {
      "id": "oai:materialscloud.org:1595"
    }, 
    "keywords": [
      "MARVEL/DD1", 
      "machine learning", 
      "NMR", 
      "resolution"
    ], 
    "publication_date": "Dec 22, 2022, 10:15:47", 
    "_files": [
      {
        "key": "code.zip", 
        "description": "Python code used to train and use the model and pre-trained model", 
        "checksum": "md5:1063ccb35b855825414891e088d2527c", 
        "size": 42391567
      }, 
      {
        "key": "MAS_datasets.zip", 
        "description": "Datasets of variable-rate MAS 1H NMR experiments on which the model is applied", 
        "checksum": "md5:1cf1c48691243aa4f1570313a26b8fab", 
        "size": 13653317
      }, 
      {
        "key": "assignment_experiments.zip", 
        "description": "NMR experiments used to determine the assignment of the proton spectra", 
        "checksum": "md5:ec2e06aef2effca4210ca36cab4c4889", 
        "size": 276635752
      }, 
      {
        "key": "probabilistic_assignment.zip", 
        "description": "Assignment probabilities of the carbon chemical shifts of molnupiravir", 
        "checksum": "md5:c6813b6bd845a76bd956f15b6555a12a", 
        "size": 186538
      }, 
      {
        "key": "Quantum_espresso.zip", 
        "description": "Quantum ESPRESSO DFT chemical shift computations to assign chemical shifts of MDMA hydrochloride", 
        "checksum": "md5:c69af26fad5f5f72293b68479583d471", 
        "size": 48800
      }
    ], 
    "references": [
      {
        "comment": "Paper in which the method is described", 
        "doi": "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)", 
        "url": "https://doi.org/10.1002/anie.202216607", 
        "type": "Journal reference"
      }, 
      {
        "comment": "Github repository containing the code", 
        "doi": "", 
        "citation": "M. Cordova, P. Moutzouri, B. Sim\u00f5es de Almeida, D. Torodii, L. Emsley, Angew. Chem. Int. Ed. 62, e202216607 (2023)", 
        "url": "https://github.com/manucordova/PIPNet", 
        "type": "Software"
      }
    ], 
    "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.", 
    "status": "published", 
    "license": "Creative Commons Attribution Share Alike 4.0 International", 
    "conceptrecid": "1594", 
    "is_last": true, 
    "mcid": "2022.180", 
    "edited_by": 339, 
    "id": "1595", 
    "owner": 339, 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:a7-59"
  }, 
  "revision": 6, 
  "created": "2022-12-21T14:48:53.537570+00:00"
}