2026-06-17T17:09:51Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:1429 2022-08-11T21:21:27Z openaire_data community-mcarchive

Jablonka, Kevin Maik Smit, Berend Jablonka, Kevin Maik Rosen, Andrew S. Smit, Berend 2022-08-11 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. application/gzip text/markdown https://doi.org/10.24435/materialscloud:qt-cj oai:materialscloud.org:1429 mcid:2022.101 eng Materials Cloud https://doi.org/10.1016/j.matt.2021.02.015 https://doi.org/10.1038/s41524-022-00796-6 https://next-gen.materialsproject.org/mofs https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:yr-mp info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode MOF ERC MARVEL nanoporous AiiDA Gas adsorption and process performance data for MOFs info:eu-repo/semantics/other