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Recent experiments show that the diffusivities of C8 and C10 n‐alkanes in ZIF‐8 are higher than those of C7 and C9, respectively. We investigated this unusual 'odd‐even' effect by simulating the adsorption of C7‐C14 n‐alkanes in ZIF‐8 using hybrid Monte Carlo molecular simulations. The resultant adsorption isotherms, guest‐host energies, isosteric heats, and chain length distributions are analyzed for trends among the n‐alkanes. ZIF‐8 cages filled with n‐alkanes are characterized using a combination of image processing and data science to quantify the differences in packing that occur with chain length. Results indicate that packing changes drastically from C7 to C8 and from C9 to C10, which is consistent with the diffusion trends previously reported in experiments. [ABSTRACT FROM AUTHOR]

The interactions between uranium and non‐innocent organic species are an essential component of fundamental uranium redox chemistry. However, they have seldom been explored in the context of multidimensional, porous materials. Uranium‐based metal–organic frameworks (MOFs) offer a new angle to study these interactions, as these self‐assembled species stabilize uranium species through immobilization by organic linkers within a crystalline framework, while potentially providing a method for adjusting metal oxidation state through coordination of non‐innocent linkers. We report the synthesis of the MOF NU‐1700, assembled from U4+‐paddlewheel nodes and catecholate‐based linkers. We propose this highly unusual structure, which contains two U4+ ions in a paddlewheel built from four linkers—a first among uranium materials—as a result of extensive characterization via powder X‐ray diffraction (PXRD), sorption, transmission electron microscopy (TEM), and thermogravimetric analysis (TGA), in addition to density functional theory (DFT) calculations. [ABSTRACT FROM AUTHOR]

Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)‐1,3‐dimethyl‐1,2‐propadiene are improved. The "glove effect" in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO2‐NHOH‐FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation. [ABSTRACT FROM AUTHOR]

Through quantum‐chemical calculations, we investigate a family of metal–organic frameworks (MOFs) containing triazolate linkers, M2X2(BBTA) (M=metal, X=bridging anion, H2BBTA=1H,5H‐benzo(1,2‐d:4,5‐d′)bistriazole), for their ability to form terminal metal–oxo sites and subsequently activate the C−H bond of methane. By varying the metal and bridging anion in the framework, we show how to significantly tune the reactivity of this series of MOFs. The electronic structure of the metal–oxo active site is analyzed for each combination of metal and bridging ligand, and we find that spin density localized on the oxo ligand is not an inherent requirement for low C−H activation barriers. For the Mn‐ and Fe‐containing frameworks, a transition from ferromagnetic to antiferromagnetic coupling between the metal binding site and terminal oxo ligand during the C−H activation process can greatly reduce the kinetic barrier, a unique case of two‐state reactivity without a change in the net spin multiplicity. [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOFs) are a class of nanoporous materials with highly tunable structures in terms of both chemical composition and topology. Due to their tunable nature, high‐throughput computational screening is a particularly appealing method to reduce the time‐to‐discovery of MOFs with desirable physical and chemical properties. In this work, a fully automated, high‐throughput periodic density functional theory (DFT) workflow for screening promising MOF candidates was developed and benchmarked, with a specific focus on applications in catalysis. As a proof‐of‐concept, we use the high‐throughput workflow to screen MOFs containing open metal sites (OMSs) from the Computation‐Ready, Experimental MOF database for the oxidative C—H bond activation of methane. The results from the screening process suggest that, despite the strong C—H bond strength of methane, the main challenge from a screening standpoint is the identification of MOFs with OMSs that can be readily oxidized at moderate reaction conditions. © 2019 Wiley Periodicals, Inc. In this work, a high‐throughput periodic density functional theory (DFT) workflow is developed for the automated screening of metal–organic frameworks for applications in catalysis. As a proof‐of‐concept, the DFT workflow is then used to screen MOFs with open metal sites from the Computation‐Ready, Experimental MOF database to identify promising candidates for methane activation. [ABSTRACT FROM AUTHOR]

We designed, synthesized, and characterized a new Zr-based metal-organic framework material, NU-1100, with a pore volume of 1.53 ccg−1 and Brunauer-Emmett-Teller (BET) surface area of 4020 m2g−1; to our knowledge, currently the highest published for Zr-based MOFs. CH4/CO2/H2 adsorption isotherms were obtained over a broad range of pressures and temperatures and are in excellent agreement with the computational predictions. The total hydrogen adsorption at 65 bar and 77 K is 0.092 g g−1, which corresponds to 43 g L−1. The volumetric and gravimetric methane-storage capacities at 65 bar and 298 K are approximately 180 vSTP/v and 0.27 g g−1, respectively. [ABSTRACT FROM AUTHOR]

This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research. [ABSTRACT FROM AUTHOR]