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

Journal Article · · Physical Chemistry Chemical Physics. PCCP
DOI: https://doi.org/10.1039/C5CP00002E · OSTI ID:1488858
 [1];  [2];  [2];  [1]
  1. Rice Univ., Houston, TX (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
+ Show Author Affiliations

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.

Research Organization:
Univ. of Minnesota, Minneapolis, MN (United States). Nanoporous Materials Genome Center
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences, and Biosciences Division
Grant/Contract Number:
FG02-12ER16362; SC0008688
OSTI ID:
1488858
Journal Information:
Physical Chemistry Chemical Physics. PCCP, Vol. 17, Issue 18; ISSN 1463-9076
Publisher:
Royal Society of ChemistryCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 27 works
Citation information provided by
Web of Science

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Cited By (5)

Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules journal April 2018
Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks journal April 2019
The lighter side of MOFs: structurally photoresponsive metal–organic frameworks journal January 2016
In silico discovery of metal-organic frameworks for precombustion CO 2 capture using a genetic algorithm journal October 2016
Inverse design of porous materials using artificial neural networks journal January 2020

Figures / Tables (17)

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