Using collective knowledge to assign oxidation states

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{
  "revision": 7, 
  "id": "516", 
  "created": "2020-08-31T11:57:13.459557+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:dq-ey", 
    "status": "published", 
    "title": "Using collective knowledge to assign oxidation states", 
    "mcid": "2020.103", 
    "license_addendum": "", 
    "_files": [
      {
        "description": "Features, labels, and models", 
        "key": "Archive.zip", 
        "size": 1239161159, 
        "checksum": "md5:c3e60f9a11680e99fa2664d8613266a5"
      }, 
      {
        "description": "Description of the content of Archive.zip", 
        "key": "README.txt", 
        "size": 2983, 
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    ], 
    "owner": 70, 
    "_oai": {
      "id": "oai:materialscloud.org:516"
    }, 
    "keywords": [
      "ERC", 
      "MOF", 
      "ML", 
      "oxidation state", 
      "LSMO", 
      "EPFL"
    ], 
    "conceptrecid": "273", 
    "is_last": true, 
    "references": [
      {
        "type": "Preprint", 
        "doi": "10.26434/chemrxiv.11604129.v1", 
        "url": "https://doi.org/10.26434/chemrxiv.11604129.v1", 
        "comment": "Preprint where the data and model is discussed.", 
        "citation": "K. M. Jablonka, D. Ongari, S. M. Moosavi, B. Smit, Using Collective Knowledge to Assign Oxidation States. ChemRxiv preprint (2020)."
      }, 
      {
        "type": "Software", 
        "doi": "10.5281/zenodo.3567274", 
        "url": "https://zenodo.org/record/3567274", 
        "comment": "Code that can be used to generate the feature matrix. ", 
        "citation": "K. M. Jablonka, D. Ongari, S. M. Moosavi, B. Smit, Zenodo (2019)."
      }, 
      {
        "type": "Software", 
        "doi": "10.5281/zenodo.3567011", 
        "url": "https://zenodo.org/record/3567011", 
        "comment": "Software that implements the code to train and test the models. ", 
        "citation": "K. M. Jablonka, D. Ongari, S. M. Moosavi, B. Smit, Zenodo (2019)."
      }, 
      {
        "type": "Journal reference", 
        "doi": "10.1038/s41557-021-00717-y", 
        "url": "https://www.nature.com/articles/s41557-021-00717-y", 
        "citation": "K. M. Jablonka, D. Ongari, S. M. Moosavi, B. Smit, Nature Chemistry 13, 771-777 (2021)"
      }
    ], 
    "publication_date": "Sep 03, 2020, 11:01:15", 
    "license": "Creative Commons Attribution 4.0 International", 
    "id": "516", 
    "description": "Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed theories to predict the oxidation state based on counting rules, which can fail to describe the oxidation states of systems such as metal-organic frameworks.\nHere we present a data-driven approach to automatically assign oxidation states, using a machine learning model trained on assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database.\nOur approach only considers the immediate local environment around a metal centre, and is robust to experimental uncertainties (like incorrect protonation, unbound solvents, or changes in bondlength).\nWe find such excellent accuracy in our predictions that we can use our method to detect incorrect assignments. \nThis work nicely illustrates how powerful the collective knowledge of chemists is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.", 
    "version": 2, 
    "contributors": [
      {
        "affiliations": [
          "Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Sion, VS, Switzerland"
        ], 
        "familyname": "Jablonka", 
        "givennames": "Kevin Maik"
      }, 
      {
        "affiliations": [
          "Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Sion, VS, Switzerland"
        ], 
        "familyname": "Ongari", 
        "givennames": "Daniele"
      }, 
      {
        "affiliations": [
          "Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Sion, VS, Switzerland"
        ], 
        "familyname": "Moosavi", 
        "givennames": "Seyed Mohamad"
      }, 
      {
        "email": "berend.smit@epfl.ch", 
        "affiliations": [
          "Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Sion, VS, Switzerland"
        ], 
        "familyname": "Smit", 
        "givennames": "Berend"
      }
    ], 
    "edited_by": 70
  }, 
  "updated": "2022-01-23T13:50:10.729574+00:00"
}