In silico discovery of covalent organic frameworks for carbon capture

DOI

We screen a database of more than 69,000 hypothetical covalent organic frameworks (COFs) for carbon capture, using parasitic energy as a metric. In order to compute CO2-framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine; over 70 are better performers than the best experimental COFs; and several perform similarly to Mg-MOF-74. We analyze the effect of pore topology on carbon capture performance in order to guide development of improved carbon capture materials.

Identifier
DOI https://doi.org/10.24435/materialscloud:2020.0029/v1
Related Identifier https://doi.org/10.1021/acsami.0c01659
Related Identifier https://www.materialscloud.org/discover/hcofs-co2
Related Identifier https://www.materialscloud.org/explore/hcofs-co2
Related Identifier https://renkulab.io/projects/new?data=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
Related Identifier https://archive.materialscloud.org/communities/mcarchive
Related Identifier https://doi.org/10.24435/materialscloud:dz-84
Metadata Access https://archive.materialscloud.org/oai2d?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:347
Provenance
Creator Deeg, Kathryn S.; Borges, Daiane Damasceno; Ongari, Daniele; Rampal, Nakul; Talirz, Leopold; Yakutovich, Aliaksandr V.; Huck, Johanna M.; Smit, Berend
Publisher Materials Cloud
Contributor Ongari, Daniele
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type info:eu-repo/semantics/other
Format text/csv; application/octet-stream; text/markdown
Discipline Materials Science and Engineering