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Pyrene is one of the most widely investigated aromatic hydrocarbons given to its unique optical and electronic properties. Hence, pyrene-based ligands have been attractive for the synthesis of metal-organic frameworks (MOFs) in the last few years. In this review, we will focus on the most important characteristics of pyrene, in addition to the development and synthesis of pyrene-based molecules as bridging ligands to be used in MOF structures. We will summarize the synthesis attempts, as well as the post-synthetic modifications of pyrene-based MOFs by the incorporation of metals or ligands in the structure. The discussion of promising results of such MOFs in several applications; including luminescence, photocatalysis, adsorption and separation, heterogeneous catalysis, electrochemical applications and bio-medical applications will be highlighted. Finally, some insights and future prospects will be given based on the studies discussed in the review. This review will pave the way for the researchers in the field for the design and development of novel pyrene-based structures and their utilization for different applications.

A computationally affordable approach, based on quasi-harmonic lattice dynamics, is presented for the quantum-mechanical calculation of thermoelastic moduli of flexible, stimuli-responsive, organic crystals. The methodology relies on the simultaneous description of structural changes induced by thermal expansion and strain. The complete thermoelastic response of the mechanically flexible metal-organic copper(II) acetylacetonate crystal is determined and discussed in the temperature range 0-300 K. The elastic moduli do not just shrink with temperature but they do so anisotropically. The present results clearly indicate the need for an explicit account of thermal effects in the simulation of mechanical properties of elastically flexible organic materials. Indeed, predictions from standard static calculations on this flexible metal-organic crystal are off by up to 100%.

Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures., At present there are databases with over 500,000 predicted or synthesized MOF structures, yet a method to establish whether a new material adds new information does not exist. Here the authors propose a machine-learning based approach to quantify the structural and chemical diversity in common MOF databases.

We report the use of two earth abundant molybdenum sulfide-based cocatalysts, Mo3S132- clusters and 1T-MoS2 nanoparticles (NPs), in combination with the visible-light active metal-organic framework (MOF) MIL-125-NH2 for the photocatalytic generation of hydrogen (H2) from water splitting. Upon irradiation (λ ≥ 420 nm), the best-performing mixtures of Mo3S132-/MIL-125-NH2 and 1T-MoS2/MIL-125-NH2 exhibit high catalytic activity, producing H2 with evolution rates of 2094 and 1454 μmol h-1 gMOF-1 and apparent quantum yields of 11.0 and 5.8% at 450 nm, respectively, which are among the highest values reported to date for visible-light-driven photocatalysis with MOFs. The high performance of Mo3S132- can be attributed to the good contact between these clusters and the MOF and the large number of catalytically active sites, while the high activity of 1T-MoS2 NPs is due to their high electrical conductivity leading to fast electron transfer processes. Recycling experiments revealed that although the Mo3S132-/MIL-125-NH2 slowly loses its activity, the 1T-MoS2/MIL-125-NH2 retains its activity for at least 72 h. This work indicates that earth-abundant compounds can be stable and highly catalytically active for photocatalytic water splitting, and should be considered as promising cocatalysts with new MOFs besides the traditional noble metal NPs.

Xenon is known to be a very efficient anesthetic gas, but its cost prohibits the wider use in medical industry and other potential applications. It has been shown that Xe recovery and recycling from anesthetic gas mixtures can significantly reduce its cost as anesthetic. The current technology uses series of adsorbent columns followed by low-temperature distillation to recover Xe; this method is expensive to use in medical facilities. Herein, we propose a much simpler and more efficient system to recover and recycle Xe from exhaled anesthetic gas mixtures at room temperature using metal-organic frameworks (MOFs). Among the MOFs tested, PCN-12 exhibits unprecedented performance with high Xe capacity and Xe/O2 , Xe/N2 and Xe/CO2 selectivity at room temperature. The in situ synchrotron measurements suggest that Xe is occupies the small pockets of PCN-12 compared to unsaturated metal centers (UMCs). Computational modeling of adsorption further supports our experimental observation of Xe binding sites in PCN-12.

Finding heterogeneous catalysts that are superior to homogeneous ones for selective catalytic transformations is a major challenge in catalysis. Here, we show how micropores in metal-organic frameworks (MOFs) push homogeneous catalytic reactions into kinetic regimes inaccessible under standard conditions. Such property allows branched selectivity up to 90% in the Co-catalysed hydroformylation of olefins without directing groups, not achievable with existing catalysts. This finding has a big potential in the production of aldehydes for the fine chemical industry. Monte Carlo and density functional theory simulations combined with kinetic models show that the micropores of MOFs with UMCM-1 and MOF-74 topologies increase the olefins density beyond neat conditions while partially preventing the adsorption of syngas leading to high branched selectivity. The easy experimental protocol and the chemical and structural flexibility of MOFs will attract the interest of the fine chemical industries towards the design of heterogeneous processes with exceptional selectivity., The Co-catalysed hydroformylation of olefins produces selectively linear but not branched aldehydes. Here, the authors show that microporous MOFs increase the olefins density in the pores beyond neat conditions allowing high branched selectivity through kinetic modulation when added to a liquid phase hydroformylation mixture.

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years., Editorial changes (typos fixed, minor adjustments to figures)

Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials’ performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications., We identify the opportunity for a computational platform for matching nanoporous materials and gas-related applications, motivating the development of automated and reproducible computational workflows.

Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can fail to describe oxidation states in extended crystalline systems such as metal-organic frameworks. Here we propose the use of a machine-learning model, trained on assignments by chemists encoded in the chemical names in the Cambridge Structural Database, to automatically assign oxidation states to the metal ions in metal-organic frameworks. In our approach, only the immediate local environment around a metal centre is considered. We show that the strategy is robust to experimental uncertainties such as incorrect protonation, unbound solvents or changes in bond length. This method gives good accuracy and we show that it can be used to detect incorrect assignments in the Cambridge Structural Database, illustrating how collective knowledge can be captured by machine learning and converted into a useful tool.

Molecular simulations with periodic boundary conditions require to define a certain cutoff distance beyond which pairwise dispersion interactions are neglected. For the simulation of homogeneous phases it is well-established to use tail-corrections, that can remedy this truncation of the potential. These corrections are built under the assumption that beyond the cutoff the radial distribution function is equal to one. In this work we shed some light on the discussion whether or not tail corrections should be used in the modelling of heterogeneous systems. We show that for the adsorption of gasses in a diverse set nanoporous crystalline materials (zeolites, Covalent Organic Frameworks (COFs), and Metal Organic Frameworks (MOFs)), tail-corrections are an appropriate choice with which the results are much less sensitive to the details of the truncation.

Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in metal-organic frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality DFT-derived DDEC charges for 2338 MOF structures. We focused on the systematic errors that are attributable to specific atom types to quantify the final precision that one can expect from Qeq methods in the context of gas adsorption where the electrostatic potential plays a significant role, namely, CO2 and H2S adsorption. In conclusion, both the type of algorithm and the input parameters have a large impact on the resulting charges, and we draw some guidelines to help the user to choose the proper combination of the two for obtaining a meaningful set of charges. We show that, considering this set of MOFs, the accuracy of the original Qeq scheme is often still comparable with the most recent variants, even if it clearly fails in the presence of certain atom types, such as alkali metals., European Union (EU); H2020; European Research Council (ERC)-2017-Starting Grant; Swedish Science Council (VR); Swiss National Supercomputing Centre (CSCS); European Research Council (ERC) European Union’s Horizon 2020 research and innovation programme; NCCR Marvel; INSPIRE Potentials Master's Fellowship; COSMOS

International audience; In this work, we report the synthesis of SION-8, a novel metal-organic framework (MOF) based on Ca(II) and a tetracarboxylate ligand TBAPy(4-) endowed with two chemically distinct types of pores characterized by their hydrophobic and hydrophilic properties. By altering the activation conditions, we gained access to two bulk materials: the fully activated SION-8F and the partially activated SION-8P with exclusively the hydrophobic pores activated. SION-8P shows high affinity for both CO2 (Q(st) = 28.4 kJ/mol) and CH4 (Q(st) = 21.4 kJ/mol), while upon full activation, the difference in affinity for CO2 (Q(st) = 23.4 kJ/mol) and CH4 (Q(st) = 16.0 kJ/mol) is more pronounced. The intrinsic flexibility of both materials results in complex adsorption behavior and greater adsorption of gas molecules than if the materials were rigid. Their CO2/CH4 separation performance was tested in fixed-bed breakthrough experiments using binary gas mixtures of different compositions and rationalized in terms of molecular interactions. SION-8F showed a 40-160% increase (depending on the temperature and the gas mixture composition probed) of the CO2/CH4 dynamic breakthrough selectivity compared to SION-8P, demonstrating the possibility to rationally tune the separation performance of a single MOF by manipulating the stepwise activation made possible by the MOF's biporous nature.

© 2017 American Chemical Society. Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable, and it can also be obtained from the refined unit cell by a number of computational techniques. In this work, we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium, and probe center pore volumes, by studying a database of more than 5000 frameworks. We developed a new technique to fully characterize the internal void of a microporous material and to compute the probe-accessible and -occupiable pore volume. We show that, unlike the other definitions of pore volume, the occupiable pore volume can be directly related to the experimentally measured pore volumes from nitrogen isotherms.

© 2017 American Chemical Society. The copper paddle-wheel is the building unit of many metal organic frameworks. Because of the ability of the copper cations to attract polar molecules, copper paddle-wheels are promising for carbon dioxide adsorption and separation. They have therefore been studied extensively, both experimentally and computationally. In this work we investigate the copper-CO2interaction in HKUST-1 and in two different cluster models of HKUST-1: monocopper Cu(formate)2and dicopper Cu2(formate)4. We show that density functional theory methods severely underestimate the interaction energy between copper paddle-wheels and CO2, even including corrections for the dispersion forces. In contrast, a multireference wave function followed by perturbation theory to second order using the CASPT2 method correctly describes this interaction. The restricted open-shell Møller-Plesset 2 method (ROS-MP2, equivalent to (2,2) CASPT2) was also found to be adequate in describing the system and used to develop a novel force field. Our parametrization is able to predict the experimental CO2adsorption isotherms in HKUST-1, and it is shown to be transferable to other copper paddle-wheel systems.

Herein, we report the performance of a photocatalytic system based on visible-light active MIL-125-NH2 mixed with nickel phosphide (Ni2P) nanoparticles. This combination boosts the H2 evolution rate to an outstanding value of 894 μmol h−1 g−1 under visible-light irradiation, which is among the highest H2 evolution rates reported to date for metal–organic frameworks (MOFs). The H2 generation rate exhibited by Ni2P/MIL-125-NH2 is almost 3 times higher than that of the Pt/MIL-125-NH2 system, highlighting the impact of the co-catalyst on photocatalytic water splitting. Additionally, our system outperforms the Ni2P/TiO2 system under UV-vis irradiation. The exceptional performance of Ni2P/MIL-125-NH2 is due to the efficient transfer of photogenerated electrons from MIL-125-NH2 to Ni2P, high intrinsic activity of Ni2P and exceptional synergy between them. This system exhibits the highest apparent quantum yields of 27.0 and 6.6% at 400 and 450 nm, respectively, ever reported for MOFs.

Δ9-Tetrahydrocannabinol (THC) is the principal psychoactive component of cannabis, and there is an urgent need to build low-cost and portable devices that can detect its presence from breath. Similarly to alcohol detectors, these tools can be used by law enforcement to determine driver intoxication and enforce safer and more regulated use of cannabis. In this work, we propose to use a class of microporous crystals, metal-organic frameworks (MOFs), to selectively adsorb THC that can be later detected using optical, electrochemical, or fluorescence-based sensing methods. We computationally screened more than 5000 MOFs, highlighting the materials that have the largest affinity with THC, as well as the highest selectivity against water, showing that it is thermodynamically feasible for MOFs to adsorb THC from humid breath. We propose and compare different models for THC and different computational protocols to rank the promising materials, also presenting a novel approach to assess the permeability of a porous framework to nonspherical molecules. We identified three adsorption motifs in MOFs with high affinity to THC, which we refer to as "narrow channels", "thick walls", and "parking spots". Therefore, we expect our protocols and our findings to be generalizable for different classes of microporous materials and also for investigating the adsorption properties of other large molecules that, like THC, have a nonspherical shape.

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of similar to 20,000 hypothetical MOFs, which are diverse in terms of their chemical design space-metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications-post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal’s postcombustion flue gases. This workflow is applied to a database of 324 covalent–organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps: (1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, (2) fitting atomic point charges based on the electron density, (3) characterizing the pore geometry of the structures before and after optimization, (4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, (5) assessing the CO2 parasitic energy via process modeling. The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported., An automated and reproducible computational workflow is proposed, to systematically optimize the geometry of covalent−organic frameworks and evaluate their performances for carbon capture and storage.

We screen a database of more than 69,000 hypothetical covalent organic frameworks (COFs) for carbon capture, using parasitic energy as a metric. In order to compute CO2-framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine. Furthermore, we identify over 70 top performers that, based on the same metrics of evaluation, perform comparably to Mg-MOF-74 and outperform reported experimental COFs for this application. We analyze the effect of pore topology on carbon capture performance in order to guide development of improved carbon capture materials.