Diversifying databases of metal organic frameworks for high-throughput computational screening

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{
  "metadata": {
    "is_last": true, 
    "version": 1, 
    "title": "Diversifying databases of metal organic frameworks for high-throughput computational screening", 
    "keywords": [
      "metal-organic frameworks", 
      "molecular simulations", 
      "machine learning", 
      "diversity", 
      "MARVEL/DD4", 
      "ERC"
    ], 
    "description": "By combining metal nodes and organic linkers, an  infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical MOFs, we need to assure that they not only contribute towards the growth of the count of structures but also add different chemistry to existing databases. In this work, we designed a database of ~20,000 hypothetical MOFs which are diverse in terms of their chemical design space\u2014metal nodes, organic linkers, functional groups and pore geometries. Using Machine Learning techniques, we visualized and quantified the diversity of these structures. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications---post combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post combustion carbon capture) and MOF-5 (for hydrogen storage).", 
    "license": "Creative Commons Attribution 4.0 International", 
    "references": [
      {
        "url": "https://pubs.acs.org/doi/10.1021/acsami.1c16220", 
        "type": "Journal reference", 
        "citation": "S. Majumdar, S. M. Moosavi, K. M. Jablonka, D. Ongari,  B. Smit, 13, 61004\u201361014 (2021)", 
        "comment": "Paper in which the work is published", 
        "doi": "10.1021/acsami.1c16220"
      }
    ], 
    "doi": "10.24435/materialscloud:yn-de", 
    "conceptrecid": "971", 
    "publication_date": "Jul 30, 2021, 18:36:08", 
    "edited_by": 482, 
    "_oai": {
      "id": "oai:materialscloud.org:972"
    }, 
    "contributors": [
      {
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland"
        ], 
        "familyname": "Majumdar", 
        "givennames": "Sauradeep"
      }, 
      {
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland"
        ], 
        "familyname": "Moosavi", 
        "givennames": "Seyed Mohamad"
      }, 
      {
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland"
        ], 
        "familyname": "Jablonka", 
        "givennames": "Kevin Maik"
      }, 
      {
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland"
        ], 
        "familyname": "Ongari", 
        "givennames": "Daniele"
      }, 
      {
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland"
        ], 
        "email": "berend.smit@epfl.ch", 
        "familyname": "Smit", 
        "givennames": "Berend"
      }
    ], 
    "owner": 482, 
    "license_addendum": null, 
    "mcid": "2021.126", 
    "_files": [
      {
        "size": 156255289, 
        "checksum": "md5:a424d0a320baaad4a97378a78d8022dd", 
        "description": "MOF structure files, MOF structure properties", 
        "key": "mof_data.tar.gz"
      }, 
      {
        "size": 959, 
        "checksum": "md5:e1b5a3069dd8c7ee1c76c05860b336de", 
        "description": "Description of the content of mof_data.tar.gz", 
        "key": "README.txt"
      }
    ], 
    "id": "972", 
    "status": "published"
  }, 
  "revision": 6, 
  "updated": "2022-10-25T09:22:57.662367+00:00", 
  "created": "2021-07-29T23:11:53.319901+00:00", 
  "id": "972"
}