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

Abstract

Metal–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. Furthermore, the correlation between deliverable capacities and surface area is network dependent.

Authors:
 [1];  [2];  [2];  [1]
  1. Rice Univ., Houston, TX (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Univ. of Minnesota, Minneapolis, MN (United States). Nanoporous Materials Genome Center
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Chemical Sciences, Geosciences & Biosciences Division
OSTI Identifier:
1488858
Grant/Contract Number:  
FG02-12ER16362; SC0008688
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Physical Chemistry Chemical Physics. PCCP (Print)
Additional Journal Information:
Journal Name: Physical Chemistry Chemical Physics. PCCP (Print); Journal Volume: 17; Journal Issue: 18; Journal ID: ISSN 1463-9076
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Bao, Yi, Martin, Richard L., Haranczyk, Maciej, and Deem, Michael W. In silico prediction of MOFs with high deliverable capacity or internal surface area. United States: N. p., 2015. Web. doi:10.1039/C5CP00002E.
Bao, Yi, Martin, Richard L., Haranczyk, Maciej, & Deem, Michael W. In silico prediction of MOFs with high deliverable capacity or internal surface area. United States. doi:10.1039/C5CP00002E.
Bao, Yi, Martin, Richard L., Haranczyk, Maciej, and Deem, Michael W. Thu . "In silico prediction of MOFs with high deliverable capacity or internal surface area". United States. doi:10.1039/C5CP00002E. https://www.osti.gov/servlets/purl/1488858.
@article{osti_1488858,
title = {In silico prediction of MOFs with high deliverable capacity or internal surface area},
author = {Bao, Yi and Martin, Richard L. and Haranczyk, Maciej and Deem, Michael W.},
abstractNote = {Metal–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. Furthermore, the correlation between deliverable capacities and surface area is network dependent.},
doi = {10.1039/C5CP00002E},
journal = {Physical Chemistry Chemical Physics. PCCP (Print)},
number = 18,
volume = 17,
place = {United States},
year = {Thu Feb 19 00:00:00 EST 2015},
month = {Thu Feb 19 00:00:00 EST 2015}
}

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Cited by: 11 works
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