High-Throughput Computational Screening of Multivariate Metal-Organic Frameworks (MTV-MOFs) for CO2 Capture

Song Li, Yongchul G. Chung, Cory M. Simon, Randall Q. Snurr*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


Multivariate metal-organic frameworks (MTV-MOFs) contain multiple linker types within a single structure. Arrangements of linkers containing different functional groups confer structural diversity and surface heterogeneity and result in a combinatorial explosion in the number of possible structures. In this work, we carried out high-throughput computational screening of a large number of computer-generated MTV-MOFs to assess their CO2 capture properties using grand canonical Monte Carlo simulations. The results demonstrate that functionalization enhances CO2 capture performance of MTV-MOFs when compared to their parent (unfunctionalized) counterparts, and the pore size plays a dominant role in determining the CO2 adsorption capabilities of MTV-MOFs irrespective of the combinations of the three functional groups (-F, -NH2, and -OCH3) that we investigated. We also found that the functionalization of parent MOFs with small pores led to larger enhancements in CO2 uptake and CO2/N2 selectivity than functionalization in larger-pore MOFs. Free energy contour maps are presented to visually compare the influence of linker functionalization between frameworks with large and small pores.

Original languageEnglish (US)
Pages (from-to)6135-6141
Number of pages7
JournalJournal of Physical Chemistry Letters
Issue number24
StatePublished - Dec 21 2017

ASJC Scopus subject areas

  • Materials Science(all)
  • Physical and Theoretical Chemistry


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