Because of their high surface areas, crystallinity, and tunable properties, metal-organic frameworks (MOFs) have attracted intense interest as next generation materials for gas capture and storage. An often-cited benefit of MOFs is their large number of possible structures and compositions. Nevertheless, this design flexibility also has drawbacks, as pinpointing optimal compounds from thousands of candidates can be time consuming and costly using conventional experimental approaches. As a consequence, computational approaches are garnering increasing importance as a means to accelerate the discovery of high-performing MOFs.
Here we demonstrate a range of computational techniques that have been applied to predict the performance of MOFs for CO2 capture and the storage of methane and hydrogen.
The techniques include:
High-throughput screening based on data-mining and empirical correlations 
Semi-empirical Monte Carlo simulations of usable capacities 
First-principles calculations of thermodynamics and electronic structure [3,4].
For CO2 capture and CH4 storage, these techniques are illustrated on metal-substituted MOFs based on M-DOBDC and M-HKUST-1, which have demonstrated amongst the highest capture/storage capacities at moderate pressures and temperatures. In the case of H2, we identify trends and promising adsorbents from amongst 4,000 known metal-organic compounds mined from the Cambridge Structure Database.
 Goldsmith et al., Chem. Mater. 25, 3373 (2013).