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

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.