Automated prediction of ground state spin for transition metal complexes

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{
  "metadata": {
    "edited_by": 576, 
    "owner": 643, 
    "_oai": {
      "id": "oai:materialscloud.org:2250"
    }, 
    "description": "Predicting the ground state spin of transition metal complexes is a challenging task. Previous attempts have been focused on specific regions of chemical space, whereas a more general automated approach is required to process crystallographic structures for high-throughput quantum chemistry computations. In this work, we developed a method to predict ground state spins of transition metal complexes. We started by constructing a dataset which contains 2,063 first row transition metal complexes taken from experimental crystal structures and their computed ground state spins. This dataset showed large chemical diversity in terms of metals, metal oxidation states, coordination geometries, and ligands. Then, we analyzed the trends between structural and electronic features of the complexes and their ground state spins, and put forward an empirical spin state assignment model. We also used simple descriptors to build a statistical model with >95% predictive accuracy across the board. With this, we provide a practical and automated way to determine the ground state spin of transition metal complex from structure, enabling the high-throughput exploration of crystallographic repositories.", 
    "mcid": "2024.100", 
    "id": "2250", 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "references": [
      {
        "citation": "Y. Cho, R. Laplaza, S. Vela, C. Corminboeuf. To be submitted.", 
        "type": "Journal reference", 
        "comment": "Paper in which the method and data are discussed."
      }
    ], 
    "doi": "10.24435/materialscloud:jx-a5", 
    "keywords": [
      "spin state", 
      "transition metal complex", 
      "transition metal", 
      "machine learning", 
      "MARVEL"
    ], 
    "contributors": [
      {
        "affiliations": [
          "Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland", 
          "National Centre for Computational Design and Discovery of Novel Materials (MARVEL), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"
        ], 
        "familyname": "Cho", 
        "givennames": "Yuri"
      }, 
      {
        "affiliations": [
          "Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland", 
          "National Centre for Competence in Research\u2013Catalysis (NCCR\u2013Catalysis), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"
        ], 
        "familyname": "Laplaza", 
        "givennames": "Ruben"
      }, 
      {
        "affiliations": [
          "Departament de Ci\u00e8ncia de Materials i Qu\u00edmica F\u00edsica and IQTCUB, Universitat de Barcelona, Barcelona, Spain"
        ], 
        "familyname": "Vela", 
        "givennames": "Sergi"
      }, 
      {
        "affiliations": [
          "Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland", 
          "National Centre for Computational Design and Discovery of Novel Materials (MARVEL), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland", 
          "National Centre for Competence in Research\u2013Catalysis (NCCR\u2013Catalysis), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"
        ], 
        "familyname": "Corminboeuf", 
        "email": "clemence.corminboeuf@epfl.ch", 
        "givennames": "Clemence"
      }
    ], 
    "conceptrecid": "1977", 
    "version": 2, 
    "publication_date": "Jul 01, 2024, 17:02:06", 
    "is_last": true, 
    "status": "published", 
    "_files": [
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        "description": "README file explaining the contents of all others.", 
        "key": "README.txt"
      }, 
      {
        "size": 15906, 
        "checksum": "md5:e88088338fb04891868f1ca246ed82e0", 
        "description": "Jupyter notebook used to generate the chemiscope files from the raw data and structures.", 
        "key": "chemiscopify.ipynb"
      }, 
      {
        "size": 1269939, 
        "checksum": "md5:0495b07263817f6f824a5204d7c6451e", 
        "description": "Ground state spin dataset.", 
        "key": "Ground_state_spin_dataset_chemiscope.json.gz"
      }, 
      {
        "size": 88305, 
        "checksum": "md5:48508b9c865985ee3cb41c9180d47445", 
        "description": "Properties of the 2032 structures in the ground state spin dataset.", 
        "key": "property_2063.txt"
      }, 
      {
        "size": 14523, 
        "checksum": "md5:e2c49b2edfd4af48c24a07041e7f3a30", 
        "description": "Crystallographic CSD refcodes of the 2063 structures in the ground state spin dataset.", 
        "key": "refcode_2063.txt"
      }, 
      {
        "size": 1539692, 
        "checksum": "md5:f22d5ea10de1670772786353a2ae0819", 
        "description": "XYZ files for the 2063 structures in the ground state spin dataset.", 
        "key": "Ground_state_spin_dataset.tar.gz"
      }, 
      {
        "size": 1219835, 
        "checksum": "md5:4c73cb40f87c41adc36223762f8424ca", 
        "description": "Supplementary ground state spin dataset.", 
        "key": "Supplementary_dataset_chemiscope.json.gz"
      }, 
      {
        "size": 48990, 
        "checksum": "md5:a8194ca2ebdd46ebee73cafb8ee7d9e9", 
        "description": "Properties of the 1838 structures in the ground state spin dataset.", 
        "key": "property_1838.txt"
      }, 
      {
        "size": 13050, 
        "checksum": "md5:08afc6d74a8913fbb7fcbbdd8145abd6", 
        "description": "Crystallographic CSD refcodes of the 1838 structures in the ground state spin dataset.", 
        "key": "refcode_1838.txt"
      }, 
      {
        "size": 1486332, 
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        "description": "XYZ files for the 1838 structures in the ground state spin dataset.", 
        "key": "Supplementary_dataset.tar.gz"
      }
    ], 
    "title": "Automated prediction of ground state spin for transition metal complexes"
  }, 
  "id": "2250", 
  "updated": "2024-07-01T15:02:06.562579+00:00", 
  "created": "2024-07-01T14:53:03.243508+00:00", 
  "revision": 2
}