Gas adsorption and process performance data for MOFs

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{
  "revision": 7, 
  "metadata": {
    "version": 1, 
    "id": "1429", 
    "title": "Gas adsorption and process performance data for MOFs", 
    "license": "Creative Commons Attribution 4.0 International", 
    "owner": 70, 
    "mcid": "2022.101", 
    "edited_by": 576, 
    "license_addendum": null, 
    "description": "Reticular chemistry provides materials designers with a practically infinite playground on different length scales. However, the space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. One promising approach to guide the design and discovery of materials is machine learning, which typically involves learning a mapping of structures onto properties from data. \nTo advance the data-driven materials discovery of metal-organic frameworks (MOFs) for gas storage and separation applications we provide a dataset of diverse gas separation properties (CO2, CH4, H2, N2, O2 isotherms); H2S, H2O, Kr, Xe Henry coefficients (computed using grand canonical Monte-Carlo with classical force fields) as well as parasitic energy for carbon capture from natural gas and a coal-fired power plant (computed using a simple process model) for the relaxed structures in the QMOF dataset with their DDEC charges.", 
    "publication_date": "Aug 11, 2022, 21:21:27", 
    "doi": "10.24435/materialscloud:qt-cj", 
    "_files": [
      {
        "size": 10048991601, 
        "description": "Export of the AiiDA database of the molecular simulations.", 
        "checksum": "md5:2ecdddb5a4bb5980405009b990f6c4d4", 
        "key": "20220804_nanoporous.tar.gz"
      }
    ], 
    "contributors": [
      {
        "email": "mail@kjablonka.com", 
        "givennames": "Kevin Maik", 
        "affiliations": [
          "Laboratory of Molecular Simulation (LSMO), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Switzerland"
        ], 
        "familyname": "Jablonka"
      }, 
      {
        "givennames": "Andrew S.", 
        "affiliations": [
          "Department of Materials Science and Engineering, University of California, Berkeley, CA, 94720, USA", 
          "Miller Institute for Basic Research in Science, University of California, Berkeley, Berkeley, CA, 94720, USA", 
          "Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA"
        ], 
        "familyname": "Rosen"
      }, 
      {
        "email": "berend.smit@epfl.ch", 
        "givennames": "Berend", 
        "affiliations": [
          "Laboratory of Molecular Simulation (LSMO), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Switzerland"
        ], 
        "familyname": "Smit"
      }
    ], 
    "is_last": true, 
    "_oai": {
      "id": "oai:materialscloud.org:1429"
    }, 
    "status": "published", 
    "conceptrecid": "1428", 
    "keywords": [
      "MOF", 
      "ERC", 
      "MARVEL", 
      "nanoporous", 
      "AiiDA"
    ], 
    "references": [
      {
        "comment": "Preprint in which the data is used.", 
        "citation": "Kevin Maik Jablonka, Andrew S. Rosen, Berend Smit, in preparation.", 
        "type": "Preprint"
      }, 
      {
        "comment": "Paper describing the input structures.", 
        "doi": "10.1016/j.matt.2021.02.015", 
        "citation": "A. S. Rosen, S. M. Iyer, D. Ray, Z. Yao, A. Aspuru-Guzik, L. Gagliardi, J. M. Notestein, and R. Q. Snurr, Machine Learning the Quantum-Chemical Properties of Metal\u2013Organic Frameworks for Accelerated Materials Discovery, Matter 4, 1578 (2021)", 
        "type": "Journal reference", 
        "url": ""
      }, 
      {
        "comment": "Paper describing the input structures.", 
        "doi": "10.1038/s41524-022-00796-6", 
        "citation": "A. S. Rosen, V. Fung, P. Huck, C. T. O\u2019Donnell, M. K. Horton, D. G. Truhlar, K. A. Persson, J. M. Notestein, and R. Q. Snurr, High-Throughput Predictions of Metal\u2013Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration, Npj Computational Materials 8, (2022).", 
        "type": "Journal reference"
      }, 
      {
        "comment": "Interactive explorer of the input structures.", 
        "url": "https://next-gen.materialsproject.org/mofs", 
        "citation": "A. S. Rosen et al. Materials Project MOF app", 
        "type": "Website"
      }
    ]
  }, 
  "created": "2022-08-05T21:14:21.369635+00:00", 
  "updated": "2022-08-11T19:21:27.548757+00:00", 
  "id": "1429"
}