High-Throughput Screening of the CoRE-MOF-2019 Database for CO2 Capture from Wet Flue Gas: A Multi-Scale Modeling Strategy.

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.

The Decisive Role of Spin States and Spin Coupling in Dictating Selective O2 Adsorption in Chromium(II) Metal-Organic Frameworks.

Invited for the cover of this issue are Sourav Pal, Gopalan Rajaraman and co-workers at the Indian Institute of Technology Bombay, the Bhabha Atomic Research Centre and the Indian Institute of Science Education and Research. The image depicts how a mixture of atmospheric gases such as CO2 , H2 , N2 and O2 can be selectively separated using a Cr metal-organic framework where spin state and spin coupling play a crucial role. Read the full text of the article at 10.1002/chem.202104526.

The Decisive Role of Spin States and Spin Coupling in Dictating Selective O2 Adsorption in Chromium(II) Metal-Organic Frameworks.

The coordinatively unsaturated chromium(II)-based Cr3 [(Cr4 Cl)3 (BTT)8 ]2 (Cr-BTT; BTT3- =1,3,5-benzenetristetrazolate) metal-organic framework (MOF) has been shown to exhibit exceptional selectivity towards adsorption of O2 over N2 /H2 . Using periodic density functional theory (DFT) calculations, we attempted to decipher the origin of this puzzling selectivity. By computing and analyzing the magnetic exchange coupling, binding energies, the partial density of states (pDOS), and adsorption isotherms for the pristine and gas-bound MOFs [(Cr4 (X)4 Cl)3 (BTT)8 ]3- (X=O2 , N2 , and H2 ), we unequivocally established the role of spin states and spin coupling in controlling the gas selectivity. The computed geometries and gas adsorption isotherms are consistent with the earlier experiments. The binding of O2 to the MOF follows an electron-transfer mechanism resulting in a CrIII superoxo species (O2 .- ) with a very strong antiferromagnetic coupling between the two centers, whereas N2 /H2 are found to weakly interact with the metal center and hence only slightly perturb the associated coupling constants. Although the gas-bound and unbound MOFs have an S=0 ground state (GS), the nature of spin the configurations and the associated magnetic exchanges are dramatically different. The binding energy and the number of oxygen molecules that can favorably bind to the Cr center were found to vary with respect to the spin state, with a significant energy margin (47.6 kJ mol-1 ). This study offers a hitherto unknown strategy of using spin state/spin couplings to control gas adsorption selectivity in MOFs.

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal-Organic Frameworks.

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal-organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO2. Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal-Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. A strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

Exploring triazine and heptazine based self assembled molecular materials through first principles investigations.

Two-dimensional materials formed from the molecular self assembly of monomers through noncovalent interactions are of great importance in designing complex nanostructures with desired properties. The carbon nitride based heterocyclic systems, triazine and heptazine, are found to be promising candidates for generating various self assembled materials through (N....H) hydrogen bonding. Here, we explored graphyne and graphdiyne-like self assembled structures for carbon nitride materials using the density functional theory calculations. We systematically investigated the monolayer structures, stacked structures in different configurations, as well as the surface assembled structures on the Au(111) surface. In all four different monolayer structures, the monomers interact through the N...H hydrogen bonding. The electronic structure results indicate that the electronic properties in these structures can be tuned through the variation in the length of the acetylinic unit. The minimum energy stacked bilayer structure of triazine based material exactly matches with the experimentally reported structure. Surface assembled studies of the triazine based system show strong interaction between the Au(111) surface and the carbon nitride monolayer. Graphical abstract Self assembled two-dimensional molecular materials as well as the surface assemblies of triazine and heptazine based precursors are computationally investigated.