Continuous fractional component Monte Carlo simulations of high-density adsorption in metal-organic frameworks

Benjamin J. Sikora; Yamil J. Colón; Randall Q. Snurr

doi:10.1080/08927022.2015.1043629

Continuous fractional component Monte Carlo simulations of high-density adsorption in metal-organic frameworks

Benjamin J. Sikora, Yamil J. Colón, Randall Q. Snurr^*

^*Corresponding author for this work

Chemical and Biological Engineering

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

The continuous fractional component Monte Carlo method, which was designed to overcome difficulties with insertions and deletions of molecules, is modified to include configurational bias Monte Carlo methods and is further extended to binary systems. The modified method is shown to correctly predict adsorption of Ar in silicalite, Xe and Kr in HKUST-1, and enantiomers in a homochiral metal-organic framework. The modified method is also found to be approximately an order of magnitude more efficient in inserting and deleting molecules than traditional configurational bias grand canonical Monte Carlo simulations in dense systems.

Original language	English (US)
Pages (from-to)	1339-1347
Number of pages	9
Journal	Molecular Simulation
Volume	41
Issue number	16-17
DOIs	https://doi.org/10.1080/08927022.2015.1043629
State	Published - Nov 2 2015

Keywords

Monte Carlo
configurational bias
enantioselective separation
metal-organic frameworks

ASJC Scopus subject areas

Chemistry(all)
Information Systems
Chemical Engineering(all)
Modeling and Simulation
Materials Science(all)
Condensed Matter Physics

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10.1080/08927022.2015.1043629

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title = "Continuous fractional component Monte Carlo simulations of high-density adsorption in metal-organic frameworks",

abstract = "The continuous fractional component Monte Carlo method, which was designed to overcome difficulties with insertions and deletions of molecules, is modified to include configurational bias Monte Carlo methods and is further extended to binary systems. The modified method is shown to correctly predict adsorption of Ar in silicalite, Xe and Kr in HKUST-1, and enantiomers in a homochiral metal-organic framework. The modified method is also found to be approximately an order of magnitude more efficient in inserting and deleting molecules than traditional configurational bias grand canonical Monte Carlo simulations in dense systems.",

keywords = "Monte Carlo, configurational bias, enantioselective separation, metal-organic frameworks",

author = "Sikora, {Benjamin J.} and Col{\'o}n, {Yamil J.} and Snurr, {Randall Q.}",

note = "Funding Information: This work was funded by the Department of Energy [grant number DE-FG02-08ER15967]. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship [grant number DGE-0824162]. Publisher Copyright: {\textcopyright} 2015 Taylor & Francis.",

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AU - Snurr, Randall Q.

N1 - Funding Information: This work was funded by the Department of Energy [grant number DE-FG02-08ER15967]. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship [grant number DGE-0824162]. Publisher Copyright: © 2015 Taylor & Francis.

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N2 - The continuous fractional component Monte Carlo method, which was designed to overcome difficulties with insertions and deletions of molecules, is modified to include configurational bias Monte Carlo methods and is further extended to binary systems. The modified method is shown to correctly predict adsorption of Ar in silicalite, Xe and Kr in HKUST-1, and enantiomers in a homochiral metal-organic framework. The modified method is also found to be approximately an order of magnitude more efficient in inserting and deleting molecules than traditional configurational bias grand canonical Monte Carlo simulations in dense systems.

AB - The continuous fractional component Monte Carlo method, which was designed to overcome difficulties with insertions and deletions of molecules, is modified to include configurational bias Monte Carlo methods and is further extended to binary systems. The modified method is shown to correctly predict adsorption of Ar in silicalite, Xe and Kr in HKUST-1, and enantiomers in a homochiral metal-organic framework. The modified method is also found to be approximately an order of magnitude more efficient in inserting and deleting molecules than traditional configurational bias grand canonical Monte Carlo simulations in dense systems.

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KW - enantioselective separation

KW - metal-organic frameworks

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Continuous fractional component Monte Carlo simulations of high-density adsorption in metal-organic frameworks

Abstract

Keywords

ASJC Scopus subject areas

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