Please use this identifier to cite or link to this item: https://doi.org/10.1038/ncomms9328
Title: In silico prediction and screening of modular crystal structures via a high-throughput genomic approach
Authors: Li, Y
Li, X 
Liu, J
Duan, F
Yu, J
Keywords: ABC 6 zeolite
alkene
methanol
natural gas
unclassified drug
zeolite
zeolite
catalyst
computer simulation
crystal structure
genomics
molecular analysis
prediction
science and technology
zeolite
Article
catalyst
computer model
crystal structure
density functional theory
gene sequence
geometry
high throughput screening
materials
molecule
prediction
structure analysis
synthesis
chemical model
chemistry
computer simulation
genomics
Computer Simulation
Genomics
High-Throughput Screening Assays
Models, Chemical
Zeolites
Issue Date: 2015
Publisher: Nature Publishing Group
Citation: Li, 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
Rights: Attribution 4.0 International
Abstract: High-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.
Source Title: Nature Communications
URI: https://scholarbank.nus.edu.sg/handle/10635/180436
ISSN: 2041-1723
DOI: 10.1038/ncomms9328
Rights: Attribution 4.0 International
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