Tandem Hydroformylation-Aldol Condensation Reaction Enabled by Zn-MOF-74.

The tandem hydroformylation-aldol condensation (tandem HF-AC) reaction offers an efficient synthetic route to the synthesis of industrially relevant products. The addition of Zn-MOF-74 to the cobalt-catalyzed hydroformylation of 1-hexene enables tandem HF-AC under milder pressure and temperature conditions than the aldox process, where zinc salts are added to cobalt-catalyzed hydroformylation reactions to promote aldol condensation. The yield of the aldol condensation products increases by up to 17 times compared to that of the homogeneous reaction without MOF and up to 5 times compared to the aldox catalytic system. Both Co2 (CO)8 and Zn-MOF-74 are required to significantly enhance the activity of the catalytic system. Density functional theory simulations and Fourier-transform infrared experiments show that heptanal, the product of hydroformylation, adsorbs on the open metal site (OMS) of Zn-MOF-74, thereby increasing the electrophilic character of the carbonyl carbon atom and facilitating the condensation.

A data-science approach to predict the heat capacity of nanoporous materials.

The heat capacity of a material is a fundamental property of great practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications, the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine learning approach, trained on density functional theory simulations, to accurately predict the heat capacity of these materials, that is, zeolites, metal-organic frameworks and covalent-organic frameworks. The accuracy of our prediction is confirmed with experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials, the heat requirement is reduced by as much as a factor of two using the correct heat capacity.

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening.

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 ∼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.

Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworks.

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.

Pyrene-based metal organic frameworks: from synthesis to applications.

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.

Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution.

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.

Thermoelasticity of Flexible Organic Crystals from Quasi-harmonic Lattice Dynamics: The Case of Copper(II) Acetylacetonate.

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%.

Understanding the diversity of the metal-organic framework ecosystem.

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.

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. 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 show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning 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. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.

In Silico Discovery of Covalent Organic Frameworks for Carbon Capture.

We screen a database of more than 69 000 hypothetical covalent organic frameworks (COFs) for carbon capture using parasitic energy as a metric. 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; more than 70 are better performers than the best experimental COFs and several perform similarly to Mg-MOF-74. We analyze the effect of pore topology on carbon capture performance to guide the development of improved carbon capture materials.

Metal-organic frameworks as kinetic modulators for branched selectivity in hydroformylation.

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.

Building a Consistent and Reproducible Database for Adsorption Evaluation in Covalent-Organic Frameworks.

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.

Applicability of Tail Corrections in the Molecular Simulations of Porous Materials.

Molecular simulations with periodic boundary conditions require the definition of a certain cutoff radius, rc, beyond which pairwise dispersion interactions are neglected. For the simulation of homogeneous phases the use of tail corrections is well-established, which can remedy this truncation of the potential. These corrections are built under the assumption that beyond rc the radial distribution function, g(r), is equal to one. In this work we shed some light on the discussion of whether tail corrections should be used in the modeling of heterogeneous systems. We show that for the adsorption of gases in a diverse set of nanoporous crystalline materials (zeolites, covalent organic frameworks, and metal-organic frameworks), tail corrections are a convenient choice to make the adsorption results less sensitive to the details of the truncation.

Can Metal-Organic Frameworks Be Used for Cannabis Breathalyzers?

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.

Evaluating Charge Equilibration Methods To Generate Electrostatic Fields in Nanoporous Materials.

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.

Biporous Metal-Organic Framework with Tunable CO2/CH4 Separation Performance Facilitated by Intrinsic Flexibility.

In this work, we report the synthesis of SION-8, a novel metal-organic framework (MOF) based on Ca(II) and a tetracarboxylate ligand TBAPy4- 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 ( Qst = 28.4 kJ/mol) and CH4 ( Qst = 21.4 kJ/mol), while upon full activation, the difference in affinity for CO2 ( Qst = 23.4 kJ/mol) and CH4 ( Qst = 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.

Photocatalytic Hydrogen Generation from a Visible-Light-Responsive Metal-Organic Framework System: Stability versus Activity of Molybdenum Sulfide Cocatalysts.

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.

Metal-Organic Frameworks Invert Molecular Reactivity: Lewis Acidic Phosphonium Zwitterions Catalyze the Aldol-Tishchenko Reaction.

The influence of metal-organic frameworks (MOFs) as additives is herein described for the reaction of n-alkyl aldehydes in the presence of methylvinylketone and triphenylphosphine. In the absence of a MOF, the expected Morita-Baylis-Hillman product, a ß-hydroxy enone, is observed. In the presence of MOFs with UMCM-1 and MOF-5 topologies, the reaction is selective to Aldol-Tishchenko products, the 1 and 3 n-alkylesters of 2-alkyl-1,3-diols, which is unprecedented in organocatalysis. The (3-oxo-2-butenyl)triphenylphosphonium zwitterion, a commonly known nucleophile, is identified as the catalytic active species. This zwitterion favors nucleophilic character in solution, whereas once confined within the framework, it becomes an electrophile yielding Aldol-Tishchenko selectivity. Computational investigations reveal a structural change in the phosphonium moiety induced by the steric confinement of the framework that makes it accessible and an electrophile.

Origin of the Strong Interaction between Polar Molecules and Copper(II) Paddle-Wheels in Metal Organic Frameworks.

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-CO2 interaction in HKUST-1 and in two different cluster models of HKUST-1: monocopper Cu(formate)2 and 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 CO2 adsorption isotherms in HKUST-1, and it is shown to be transferable to other copper paddle-wheel systems.

Xenon Recovery at Room Temperature using Metal-Organic Frameworks.

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.