Stabilizing the escalating CO2 levels in the
atmosphere
is a grand challenge in view of the increasing global demand for energy,
the majority of which currently comes from the burning of fossil fuels.
Capturing CO2 from point source emissions using solid adsorbents
may play a part in meeting this challenge, and metal–organic
frameworks (MOFs) are considered to be a promising class of materials
for this purpose. It is important to consider the co-adsorption of
water when designing materials for CO2 capture from post-combustion
flue gases. Computational high-throughput screening (HTS) is a powerful
tool to identify top-performing candidates for a particular application
from a large material database. Using a multi-scale modeling strategy
that includes a machine learning model, density functional theory
(DFT) calculations, force field (FF) optimization, and grand canonical
Monte Carlo (GCMC) simulations, we carried out a systematic computational
HTS of the all-solvent-removed version of the computation-ready experimental
metal–organic framework (CoRE-MOF-2019) database for selective
adsorption of CO2 from a wet flue gas mixture. After initial
screening based on the pore diameters, a total of 3703 unique MOFs
from the database were considered for screening based on the FF interaction
energies of CO2, N2, and H2O molecules
with the MOFs. MOFs showing stronger interactions with CO2 compared to that with H2O and N2 were considered
for the next level of screening based on the interaction energies
calculated from DFT. CO2-selective MOFs from DFT screening
were further screened using two-component (CO2 and N2) and finally three-component (CO2, N2, and H2O) GCMC simulations to predict the CO2 capacity and CO2/N2 selectivity. Our screening
study identified MOFs that show selective CO2 adsorption
under wet flue gas conditions with significant CO2 uptake
capacity and CO2/N2 selectivity in the presence
of water vapor. We also analyzed the nature of pore confinements responsible
for the observed CO2 selectivity