A data-science approach to predict the heat capacity of nanoporous materials


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{
  "metadata": {
    "is_last": true, 
    "publication_date": "Apr 13, 2022, 17:00:09", 
    "edited_by": 576, 
    "version": 1, 
    "license": "Creative Commons Attribution 4.0 International", 
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    "mcid": "2022.53", 
    "keywords": [
      "MOF", 
      "zeolite", 
      "machine learning", 
      "thermal properties", 
      "heat capacity", 
      "SNSF", 
      "Horizon2020"
    ], 
    "contributors": [
      {
        "givennames": "Seyed Mohamad", 
        "email": "seyedmohamad.moosavi@fu-berlin.de", 
        "familyname": "Moosavi", 
        "affiliations": [
          "Laboratory of Molecular Simulation, Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland", 
          "Department of Mathematics and Computer Science, Freie Universit\u00e4t Berlin, Arnimallee 6, 14195 Berlin, Germany"
        ]
      }, 
      {
        "givennames": "Bal\u00e1zs \u00c1lmos", 
        "familyname": "Novotny", 
        "affiliations": [
          "Laboratory of Molecular Simulation, Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland"
        ]
      }, 
      {
        "givennames": "Daniele", 
        "familyname": "Ongari", 
        "affiliations": [
          "Laboratory of Molecular Simulation, Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland"
        ]
      }, 
      {
        "givennames": "Elias", 
        "familyname": "Moubarak", 
        "affiliations": [
          "Laboratory of Molecular Simulation, Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland"
        ]
      }, 
      {
        "givennames": "Mehrdad", 
        "familyname": "Asgari", 
        "affiliations": [
          "Laboratory of Molecular Simulation, Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland"
        ]
      }, 
      {
        "givennames": "\u00d6zge", 
        "familyname": "Kadioglu", 
        "affiliations": [
          "Laboratory of Molecular Simulation, Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland"
        ]
      }, 
      {
        "givennames": "Charithea", 
        "familyname": "Charalambous", 
        "affiliations": [
          "The Research Centre for Carbon Solutions (RCCS), School of Engineering and Physical Sciences, Heriot-Watt University, EH14 4AS Edinburgh, United Kingdom"
        ]
      }, 
      {
        "givennames": "Andres", 
        "familyname": "Ortega-Guerrero", 
        "affiliations": [
          "Laboratory of Molecular Simulation, Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland"
        ]
      }, 
      {
        "givennames": "Amir H.", 
        "familyname": "Farmahini", 
        "affiliations": [
          "Department of Chemical Engineering and Analytical Science, School of Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom"
        ]
      }, 
      {
        "givennames": "Lev", 
        "familyname": "Sarkisov", 
        "affiliations": [
          "Department of Chemical Engineering and Analytical Science, School of Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom"
        ]
      }, 
      {
        "givennames": "Susana", 
        "familyname": "Garcia", 
        "affiliations": [
          "The Research Centre for Carbon Solutions (RCCS), School of Engineering and Physical Sciences, Heriot-Watt University, EH14 4AS Edinburgh, United Kingdom"
        ]
      }, 
      {
        "givennames": "Frank", 
        "familyname": "No\u00e9", 
        "affiliations": [
          "Department of Mathematics and Computer Science, Freie Universit\u00e4t Berlin, Arnimallee 6, 14195 Berlin, Germany"
        ]
      }, 
      {
        "givennames": "Berend", 
        "email": "berend.smit@epfl.ch", 
        "familyname": "Smit", 
        "affiliations": [
          "Laboratory of Molecular Simulation, Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland"
        ]
      }
    ], 
    "status": "published", 
    "doi": "10.24435/materialscloud:p1-2y", 
    "title": "A data-science approach to predict the heat capacity of nanoporous materials", 
    "id": "1284", 
    "description": "The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the energy requirement is reduced by as much as a factor of two using the correct heat capacity.", 
    "owner": 693, 
    "_oai": {
      "id": "oai:materialscloud.org:1284"
    }, 
    "conceptrecid": "1283", 
    "references": [
      {
        "citation": "S.M. Moosavi, et al. (2020) submitted.", 
        "type": "Preprint"
      }
    ]
  }, 
  "updated": "2022-04-13T15:00:09.994103+00:00", 
  "revision": 11, 
  "id": "1284", 
  "created": "2022-03-12T14:22:04.734513+00:00"
}