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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]

The effective detoxification of chemical warfare agents, specifically nerve agents, is a pressing issue in the modern world. Due to the high toxicity of these molecules, simulants are often used in experiments as substitutes for the agents. However, there is little reason to believe that the current simulants used in the literature are optimal predictors of nerve agent reactivity. Density functional theory calculations were performed on the alkaline hydrolysis of over 100 organophosphate molecules to identify improved simulants for the G‐series nerve agents soman and sarin, based on low toxicity and similarity to nerve agent hydrolysis energetics and degradation mechanism. This screening highlighted 5 molecules that have nearly identical reaction barriers to the actual agents, while being far less toxic. Quantitative structure–activity relationship (QSAR) models were also derived to determine the most significant molecular descriptors for describing the hydrolysis free energy barriers of these reactions. The optimal QSAR model was subjected to a thorough statistical analysis and validation procedure to confirm its predictive capacity, showing excellent quantitative and ranking accuracy. It was further shown that the model trained on G‐series agents can reliably predict energetics for other organophosphate classes as well, including VX. Through these computational insights, experimentalists may be aided in accurately and safely studying these reactions with less toxic simulants. [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]

Abstract: DNA G‐quadruplex structures were recently discovered to provide reliable scaffolding for two‐dimensional organic frameworks due to the strong hydrogen‐bonding ability of guanine. Herein, 2,7‐diaryl pyrene building blocks with high HOMO energies and large optical gaps are incorporated into G‐quadruplex organic frameworks. The adjustable substitution on the aryl groups provides an opportunity to elucidate the framework formation mechanism; molecular non‐planarity is found to be beneficial for restricting interlayer slippage, and the framework crystallinity is highest when intermolecular interaction and non‐planarity strike a fine balance. When guanine‐functionalized pyrenes are co‐crystallized with naphthalene diimide, charge‐transfer (CT) complexes are obtained. The photophysical properties of the pyrene‐only and CT frameworks are characterized by UV/Vis and steady‐state and time‐resolved photoluminescence spectroscopies, and by EPR spectroscopy for the CT complex frameworks. [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]

Grand canonical Monte Carlo (GCMC) simulations with configurational biasing were used to study the enantioselective adsorption of four alkanols in a homochiral metal-organic framework, known as hybrid organic-inorganic zeolite analogue HOIZA-1. Conventional GCMC simulations are not able to converge satisfactorily for this system due to the tight fit of the chiral alcohols in the narrow pores. However, parallel tempering and parallel mole-fraction GCMC simulations overcome this problem. The simulations show that the enantioselective adsorption of the different (R,S)-alkanols is due to the specific geometry of the chiral molecules relative to the pore size and shape. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2324-2334, 2014 [ABSTRACT FROM AUTHOR]

Selective adsorption of CO2 over N2 is important in the design and selection of adsorbents such as metal-organic frameworks (MOFs) for CO2 capture and sequestration. In this work, single-component and mixture adsorption isotherms were calculated in MOFs using grand canonical Monte Carlo (GCMC) simulations at conditions relevant for CO2 capture from flue gas. Mixture results predicted from single-component isotherms plus ideal adsorbed solution theory (IAST) agree well with those calculated from full GCMC mixture simulations. This suggests that IAST can be used for preliminary screening of MOFs for CO2 capture as an alternative to more time-consuming mixture simulations or experiments. © 2011 Canadian Society for Chemical Engineering [ABSTRACT FROM AUTHOR]

A variety of metal-organic frameworks (MOFs) with varying linkers, topologies, pore sizes, and metal atoms were screened for xenon/krypton separation using grand canonical Monte Carlo (GCMC) simulations. The results indicate that small pores with strong adsorption sites are desired to preferentially adsorb xenon over krypton in multicomponent adsorption. However, if the pore size is too small, it can significantly limit overall gas uptake, which is undesirable. Based on our simulations, MOF-505 was identified as a promising material due to its increased xenon selectivity over a wider pressure range compared with other MOFs investigated. © 2010 American Institute of Chemical Engineers AIChE J, 2011 [ABSTRACT FROM AUTHOR]

Modeling and simulation at different scales were used to study mass transport and residence times of particles in nanostructured membranes with uniform cylindrical pores of 10-150 nm diameter and up to 5 µm long. Analytical equations of the possible mass-transport mechanisms inside the pores were used to determine that diffusion dominates over convection under the conditions of interest for selective oxidation: 700 K and pressure near atmospheric. Molecular dynamics simulations showed that surface diffusion is present only at temperatures < 700 K. Knudsen diffusion was identified as the dominant mechanism. Simulations based on its principles were performed using an ensemble of particles in a boundary-driven simulation cell, providing the average number of hits between a particle and the pore wall and the dependency of the residence time on the pore dimensions. The differences between operating a nanostructured membrane reactor in sweep-gas and pass-through modes were also investigated. [ABSTRACT FROM AUTHOR]

Describes nanoporous metal-organic materials used in advanced separation processes. Components of nanoporous metal-organic materials; Examples of ligand-bridged metal-organic materials synthesized in a directed-assembly process; Objectives in the synthesis of new materials for separations, catalysis and other applications; Role of molecular modeling in screening new structures.

Adsorption in the zeolite silicalite from binary liquid mixtures of p-xylene, m-xylene, and toluene was investigated using grand canonical Monte Carlo (GCMC) simulations. The results obtained agree well with experimental excess adsorption isotherms from the literature. The agreement is very good when the zeolite is modeled using the PARA form of the silicalite structure, but the results obtained with the native ORTHO structure are in some cases even qualitatively wrong. This supports the previous suggestion that the structure of siliealite undergoes a transition from ORTHO to PARA upon adsorption of aromatic molecules. Molecular-level details of the energetics and siting within the zeolite provide insights into the macroscopic behavior. The simulated single-component and binary results were used to test ideal adsorbed solution theory for these systems. [ABSTRACT FROM AUTHOR]

Highlights from the article: Through quantitative structure-activity relationship modeling and statistical analysis, the most significant molecular descriptors for describing the energetics of alkaline organophosphate hydrolysis reactions are also determined. DFT, hydrolysis, nerve agents, QSAR, simulants.

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]