Gas adsorption and process performance data for MOFs

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Jablonka, Kevin Maik</dc:creator>
  <dc:creator>Rosen, Andrew S.</dc:creator>
  <dc:creator>Smit, Berend</dc:creator>
  <dc:date>2022-08-11</dc:date>
  <dc: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. 
To 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.</dc:description>
  <dc:identifier>https://materialscloud-archive-failover.cineca.it/record/2022.101</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:qt-cj</dc:identifier>
  <dc:identifier>mcid:2022.101</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1429</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>MOF</dc:subject>
  <dc:subject>ERC</dc:subject>
  <dc:subject>MARVEL</dc:subject>
  <dc:subject>nanoporous</dc:subject>
  <dc:subject>AiiDA</dc:subject>
  <dc:title>Gas adsorption and process performance data for MOFs</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>