Using collective knowledge to assign oxidation states

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{
  "revision": 2, 
  "id": "274", 
  "created": "2020-05-12T13:53:31.440814+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:2019.0085/v1", 
    "status": "published", 
    "title": "Using collective knowledge to assign oxidation states", 
    "mcid": "2019.0085/v1", 
    "license_addendum": "", 
    "_files": [
      {
        "description": "datapackage containing features, labels, CSD codes, feature names and pre-trained models. ", 
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    "owner": 70, 
    "_oai": {
      "id": "oai:materialscloud.org:274"
    }, 
    "keywords": [
      "ERC", 
      "MOF", 
      "ML", 
      "oxidation state", 
      "LSMO", 
      "EPFL"
    ], 
    "conceptrecid": "273", 
    "is_last": false, 
    "references": [
      {
        "type": "Journal reference", 
        "doi": "", 
        "url": "", 
        "comment": "", 
        "citation": "K. M. Jablonka, D. Ongari, S. M. Moosavi, B. Smit, submitted, 2019. "
      }, 
      {
        "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. "
      }
    ], 
    "publication_date": "Dec 11, 2019, 00:00:00", 
    "license": "Creative Commons Attribution 4.0 International", 
    "id": "274", 
    "description": "Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (>98%) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.", 
    "version": 1, 
    "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": 98
  }, 
  "updated": "2020-09-03T09:01:15.508186+00:00"
}