In silico prediction of MOFs with high deliverable capacity or internal surface area

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dc.citation.firstpage11962
dc.citation.journalTitlePhysical Chemistry Chemical Physics
dc.citation.lastpage11973
dc.citation.volumeNumber17
dc.contributor.authorBao, Yi
dc.contributor.authorMartin, Richard L.
dc.contributor.authorHaranczyk, Maciej
dc.contributor.authorDeem, Michael W.
dc.date.accessioned2015-06-15T18:57:50Z
dc.date.available2015-06-15T18:57:50Z
dc.date.issued2015
dc.description.abstractMetal–organic frameworks (MOFs) offer unprecedented atom-scale design and structural tunability, largely due to the vast number of possible organic linkers which can be utilized in their assembly. Exploration of this space of linkers allows identification of ranges of achievable material properties as well as discovery of optimal materials for a given application. Experimental exploration of the linker space has to date been quite limited due to the cost and complexity of synthesis, while high-throughput computational studies have mainly explored MOF materials based on known or readily available linkers. Here an evolutionary algorithm for de novo design of organic linkers for metal–organic frameworks is used to predict MOFs with either high methane deliverable capacity or methane accessible surface area. Known chemical reactions are applied in silico to a population of linkers to discover these MOFs. Through this design strategy, MOF candidates are found in the ten symmetric networks acs, cds, dia, hxg, lvt, nbo, pcu, rhr, sod, and tbo. The correlation between deliverable capacities and surface area is network dependent.
dc.identifier.citationBao, Yi, Martin, Richard L., Haranczyk, Maciej, et al.. "In silico prediction of MOFs with high deliverable capacity or internal surface area." <i>Physical Chemistry Chemical Physics,</i> 17, (2015) Royal Society of Chemistry: 11962-11973. http://dx.doi.org/10.1039/C5CP00002E.
dc.identifier.doihttp://dx.doi.org/10.1039/C5CP00002E
dc.identifier.urihttps://hdl.handle.net/1911/80763
dc.language.isoeng
dc.publisherRoyal Society of Chemistry
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
dc.titleIn silico prediction of MOFs with high deliverable capacity or internal surface area
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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