Please use this identifier to cite or link to this item: https://doi.org/10.1038/ncomms9328
DC FieldValue
dc.titleIn silico prediction and screening of modular crystal structures via a high-throughput genomic approach
dc.contributor.authorLi, Y
dc.contributor.authorLi, X
dc.contributor.authorLiu, J
dc.contributor.authorDuan, F
dc.contributor.authorYu, J
dc.date.accessioned2020-10-26T08:58:42Z
dc.date.available2020-10-26T08:58:42Z
dc.date.issued2015
dc.identifier.citationLi, Y, Li, X, Liu, J, Duan, F, Yu, J (2015). In silico prediction and screening of modular crystal structures via a high-throughput genomic approach. Nature Communications 6 : 8328. ScholarBank@NUS Repository. https://doi.org/10.1038/ncomms9328
dc.identifier.issn2041-1723
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/180436
dc.description.abstractHigh-throughput computational methods capable of predicting, evaluating and identifying promising synthetic candidates with desired properties are highly appealing to today's scientists. Despite some successes, in silico design of crystalline materials with complex three-dimensionally extended structures remains challenging. Here we demonstrate the application of a new genomic approach to ABC-6 zeolites, a family of industrially important catalysts whose structures are built from the stacking of modular six-ring layers. The sequences of layer stacking, which we deem the genes of this family, determine the structures and the properties of ABC-6 zeolites. By enumerating these gene-like stacking sequences, we have identified 1,127 most realizable new ABC-6 structures out of 78 groups of 84,292 theoretical ones, and experimentally realized 2 of them. Our genomic approach can extract crucial structural information directly from these gene-like stacking sequences, enabling high-throughput identification of synthetic targets with desired properties among a large number of candidate structures. © 2015 Macmillan Publishers Limited. All rights reserved.
dc.publisherNature Publishing Group
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectABC 6 zeolite
dc.subjectalkene
dc.subjectmethanol
dc.subjectnatural gas
dc.subjectunclassified drug
dc.subjectzeolite
dc.subjectzeolite
dc.subjectcatalyst
dc.subjectcomputer simulation
dc.subjectcrystal structure
dc.subjectgenomics
dc.subjectmolecular analysis
dc.subjectprediction
dc.subjectscience and technology
dc.subjectzeolite
dc.subjectArticle
dc.subjectcatalyst
dc.subjectcomputer model
dc.subjectcrystal structure
dc.subjectdensity functional theory
dc.subjectgene sequence
dc.subjectgeometry
dc.subjecthigh throughput screening
dc.subjectmaterials
dc.subjectmolecule
dc.subjectprediction
dc.subjectstructure analysis
dc.subjectsynthesis
dc.subjectchemical model
dc.subjectchemistry
dc.subjectcomputer simulation
dc.subjectgenomics
dc.subjectComputer Simulation
dc.subjectGenomics
dc.subjectHigh-Throughput Screening Assays
dc.subjectModels, Chemical
dc.subjectZeolites
dc.typeArticle
dc.contributor.departmentDEPT OF CHEMICAL & BIOMOLECULAR ENGG
dc.description.doi10.1038/ncomms9328
dc.description.sourcetitleNature Communications
dc.description.volume6
dc.description.page8328
dc.published.statepublished
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