This work is devoted to the development of quantitative structure–property relationship (QSPR) models using machine learning to predict CO2 working capacity and CO2/H2 selectivity for precombustion carbon capture using a topologically diverse database of hypothetical metal–organic framework (MOF) structures (358 400 MOFs, 1166 network topologies). Such a diversity of the networks topology is much higher than previously used (<20 network topologies) for rapid and accurate recognition of high-performing MOFs for other gas-separation applications. The gradient boosted trees regression method allowed us to use 80% of the database as a training set, while the rest was used for the validation and test set. The QSPR models are first built using purely geometric descriptors of MOFs such as gravimetric surface area and void fraction. Additional models which account for chemical features of MOFs are constructed using atomic property weighted radial distribution functions (AP-RDFs) with a novel normalization to accommodate the size diversity of the MOF database. It is shown that the best models for CO2 working capacities (R 2 = 0.944) and CO2/H2 selectivities (R 2 = 0.872) are built from a combination of six geometric descriptors and three AP-RDF descriptors. However, more important is that our QSPR models can identify top 1000 high-performing MOFs in just top 3000 or 5000 MOFs. This work shows that QSPR modeling can account for the topological diversity of MOFs and accelerate the screening for top-performing MOFs for precombustion carbon capture.

A new porous Ni-MOF displays high selectivity and working capacity for CH4 adsorption over N2 under ambient conditions.

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Clathrate hydrate phases of Cl and Br guest molecules have been known for about 200 years. The crystal structure of these phases was recently re-determined with high accuracy by single crystal X-ray diffraction. In these structures, the water oxygen-halogen atom distances are determined to be shorter than the sum of the van der Waals radii, which indicates the action of some type of non-covalent interaction between the dihalogens and water molecules. Given that in the hydrate phases both lone pairs of each water oxygen atom are engaged in hydrogen bonding with other water molecules of the lattice, the nature of the oxygen-halogen interactions may not be the standard halogen bonds characterized recently in the solid state materials and enzyme-substrate compounds. The nature of the halogen-water interactions for the Cl and Br molecules in two isolated clathrate hydrate cages has recently been studied with ab initio calculations and Natural Bond Order analysis (Ochoa-Resendiz et al. J. Chem. Phys. 2016, 145, 161104). Here we present the results of ab initio calculations and natural localized molecular orbital analysis for Cl and Br guests in all cage types observed in the cubic structure I and tetragonal structure I clathrate hydrates to characterize the orbital interactions between the dihalogen guests and water. Calculations with isolated cages and cages with one shell of coordinating molecules are considered. The computational analysis is used to understand the nature of the halogen bonding in these materials and to interpret the guest positions in the hydrate cages obtained from the X-ray crystal structures.