High-throughput computational screening of metal-organic frameworks

The changing state of porous materials

Porous materials contain regions of empty space into which guest molecules can be selectively adsorbed and sometimes chemically transformed. This has made them useful in both industrial and domestic applications, ranging from gas separation, energy storage and ion exchange to heterogeneous catalysis and green chemistry. Porous materials are often ordered (crystalline) solids. Order-or uniformity-is frequently held to be advantageous, or even pivotal, to our ability to engineer useful properties in a rational way. Here we highlight the growing evidence that topological disorder can be useful in creating alternative properties in porous materials. In particular, we highlight here several concepts for the creation of novel porous liquids, rationalize routes to porous glasses and provide perspectives on applications for porous liquids and glasses.

Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites

Interfacial encoded properties of polymer adlayers adsorbed on the graphene (GE) and silicon dioxide (SiO2) have been constituted a scaffold for the creation of new materials. The holistic understanding of nanoscale intermolecular interaction of 1D/2D polymer assemblies on substrate is the key to bottom-up design of molecular devices. We develop an integrated multidisciplinary approach based on electronic structure computation [density functional theory (DFT)] and big data mining [machine learning (ML)] in parallel with neural network (NN) and statistical analysis (SA) to design hybrid polymers from assembly on substrate. Here we demonstrate that interfacial pressure and structural deformation of polymer network adsorbed on GE and SiO2 offer unique directions for the fabrication of 1D/2D polymers using only a small number of simple molecular building blocks. Our findings serve as the platform for designing a wide range of typical inorganic heterostructures, involving noncovalent intermolecular interaction observed in many nanoscale electronic devices.

An upper bound visualization of design trade-offs in adsorbent materials for gas separations: alkene/alkane adsorbents

The last 20 years has seen an explosion in the number of publications investigating porous solids for gas adsorption and separation. The combination of external drivers such as anthropogenic climate change and industrial efficiency has been coupled with discovery of new materials such as synthetic zeolites, metal-organic frameworks, covalent organic frameworks, and non-porous adsorbents. Numerous reviews catalogue these materials and their properties. However, the field lacks a unifying resource to visually compare and analyse materials properties with regard to their utility as a scientific advance and potential for industrial use. In the related field of membrane science, the 'Robeson upper bound' empirically describes the trade-off between gas permeability and selectivity and has become a ubiquitous tool for comparing membrane materials. In this article, we propose upper and lower bounds that empirically correlate the trade-offs encountered when designing adsorbent materials for gas separation, specifically: capacity, selectivity, and heat of adsorption. We apply bound visualizations to adsorbents studied for light alkene/alkane separations and highlight their use in identifying candidate materials for examination within process models and for guiding insights to the most effective materials design strategies. Furthermore, we note the limitations of upper and lower bound visualizations and provide links to a database resource for researchers to produce and download bound visualization plots. We anticipate that introducing bound visualizations to the field of adsorbents for gas separations will allow researchers to provide context for the importance of new materials discoveries, understand trade-offs in adsorbent design, and connect process engineers with candidate materials.

Current Trends in Metal-Organic and Covalent Organic Framework Membrane Materials

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have been thoroughly investigated with regards to applications in gas separation membranes in the past years. More recently, new preparation methods for MOFs and COFs as particles and thin-film membranes, as well as for mixed-matrix membranes (MMMs) have been developed. We will highlight novel processes and highly functional materials: Zeolitic imidazolate frameworks (ZIFs) can be transformed into glasses and we will give an insight into their use for membranes. In addition, liquids with permanent porosity offer solution processability for the manufacture of extremely potent MMMs. Also, MOF materials influenced by external stimuli give new directions for the enhancement of performance by in situ techniques. Presently, COFs with their large pores are useful in quantum sieving applications, and by exploiting the stacking behavior also molecular sieving COF membranes are possible. Similarly, porous polymers can be constructed using MOF templates, which then find use in gas separation membranes.

Screening Metal-Organic Frameworks for Separation of Binary Solvent Mixtures by Compact NMR Relaxometry

Metal–organic frameworks (MOFs) have great potential as an efficient alternative to current separation and purification procedures of a large variety of solvent mixtures—a critical process in many applications. Due to the huge number of existing MOFs, it is of key importance to identify high-throughput analytical tools, which can be used for their screening and performance ranking. In this context, the present work introduces a simple, fast, and inexpensive approach by compact low-field proton nuclear magnetic resonance (NMR) relaxometry to investigate the efficiency of MOF materials for the separation of a binary solvent mixture. The mass proportions of two solvents within a particular solvent mixture can be quantified before and after separation with the help of a priori established correlation curves relating the effective transverse relaxation times T_2eff and the mass proportions of the two solvents. The new method is applied to test the separation efficiency of powdered UiO-66(Zr) for various solvent mixtures, including linear and cyclic alkanes and benzene derivate, under static conditions at room temperature. Its reliability is demonstrated by comparison with results from ¹H liquid-state NMR spectroscopy.

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the biases of training data derived from density functional theory (DFT) and leads to many attempted calculations that are doomed to fail. Many compelling functional materials and catalytic processes involve strained chemical bonds, open-shell radicals and diradicals, or metal-organic bonds to open-shell transition-metal centers. Although promising targets, these materials present unique challenges for electronic structure methods and combinatorial challenges for their discovery. In this Perspective, we describe the advances needed in accuracy, efficiency, and approach beyond what is typical in conventional DFT-based ML workflows. These challenges have begun to be addressed through ML models trained to predict the results of multiple methods or the differences between them, enabling quantitative sensitivity analysis. For DFT to be trusted for a given data point in a high-throughput screen, it must pass a series of tests. ML models that predict the likelihood of calculation success and detect the presence of strong correlation will enable rapid diagnoses and adaptation strategies. These "decision engines" represent the first steps toward autonomous workflows that avoid the need for expert determination of the robustness of DFT-based materials discoveries.

Effect of Metal-Organic Framework (MOF) Database Selection on the Assessment of Gas Storage and Separation Potentials of MOFs

Development of computation-ready metal-organic framework databases (MOF DBs) has accelerated high-throughput computational screening (HTCS) of materials to identify the best candidates for gas storage and separation. These DBs were constructed using structural curations to make MOFs directly usable for molecular simulations, which caused the same MOF to be reported with different structural features in different DBs. We examined thousands of common materials of the two recently updated, very widely used MOF DBs to reveal how structural discrepancies affect simulated CH4 , H2 , CO2 uptakes and CH4 /H2 separation performances of MOFs. Results showed that DB selection has a significant effect on the calculated gas uptakes and ideal selectivities of materials at low pressure. A detailed analysis on the curated structures was provided to isolate the critical elements of MOFs determining the gas uptakes. Identification of the top-performing materials for gas separation was shown to strongly depend on the DB used in simulations.

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.

Gas Storage in Porous Molecular Materials

Molecules with permanent porosity in the solid state have been studied for decades. Porosity in these systems is governed by intrinsic pore space, as in cages or macrocycles, and extrinsic void space, created through loose, intermolecular solid-state packing. The development of permanently porous molecular materials, especially cages with organic or metal-organic composition, has seen increased interest over the past decade, and as such, incredibly high surface areas have been reported for these solids. Despite this, examples of these materials being explored for gas storage applications are relatively limited. This minireview outlines existing molecular systems that have been investigated for gas storage and highlights strategies that have been used to understand adsorption mechanisms in porous molecular materials.

Engineering entangled photon pairs with metal-organic frameworks

The discovery and design of new materials with competitive optical frequency conversion efficiencies can accelerate the development of scalable photonic quantum technologies. Metal-organic framework (MOF) crystals without inversion symmetry have shown potential for these applications, given their nonlinear optical properties and the combinatorial number of possibilities for MOF self-assembly. In order to accelerate the discovery of MOF materials for quantum optical technologies, scalable computational assessment tools are needed. We develop a multi-scale methodology to study the wavefunction of entangled photon pairs generated by selected non-centrosymmetric MOF crystals via spontaneous parametric down-conversion (SPDC). Starting from an optimized crystal structure, we predict the shape of the G (2) intensity correlation function for coincidence detection of the entangled pairs, produced under conditions of collinear type-I phase matching. The effective nonlinearities and photon pair correlation times obtained are comparable to those available with inorganic crystal standards. Our work thus provides fundamental insights into the structure-property relationships for entangled photon generation with metal-organic frameworks, paving the way for the automated discovery of molecular materials for optical quantum technology.

Nanoscale Zeolitic Imidazolate Framework-8 Activator of Canonical MAPK Signaling for Bone Repair

Zeolitic imidazolate framework-8 (ZIF-8) is an important type of metal organic framework and has found numerous applications in the biomedical field. Our previous studies have demonstrated that nano ZIF-8-based titanium implants could promote osseointegration; however, its osteogenic capacity and the related mechanisms in bone regeneration have not been fully clarified. Presented here is a nanoscale ZIF-8 that could drive rat bone mesenchymal stem cell (rBMSC) differentiation into osteoblasts both in vitro and in vivo, and interestingly, nano ZIF-8 exhibited a better osteogenic effect compared with ionic conditions of Zn at the same concentration of Zn2+. Moreover, the cellular uptake mechanisms of the nanoparticles were thoroughly clarified. Specifically, nano ZIF-8 could enter the rBMSC cytoplasm probably via caveolae-mediated endocytosis and macropinocytosis. The intracellular and extracellular Zn2+ released from nano ZIF-8 and the receptors involved in the endocytosis may play a role in inducing activation of key osteogenic pathways. Furthermore, through transcriptome sequencing, multiple osteogenic pathways were found to be upregulated, among which nano ZIF-8 primarily phosphorylated ERK, thus activating the canonical mitogen-activated protein kinase pathway and promoting the osteogenesis of rBMSCs. Taken together, this study helps to elucidate the mechanism by which nano ZIF-8 regulates osteogenesis and suggests it to be a potential biomaterial for constructing multifunctional composites in bone tissue engineering.

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre- and post-synthetic computer simulations are mostly carried out in a piecemeal and ad hoc manner. Whilst high throughput approaches have been used for more than 30 years in the porous material fields, routine integration of experimental and computational processes is only now becoming more established. Herein, important developments are highlighted and emerging challenges for the community identified, including the need to work toward more integrated workflows.

Design of Metal-Organic Framework Templated Materials Using High-Throughput Computational Screening

The ability to crosslink Metal-Organic Frameworks (MOFs) has recently been discovered as a flexible approach towards synthesizing MOF-templated “ideal network polymers”. Crosslinking MOFs with rigid cross-linkers would allow the synthesis of crystalline Covalent-Organic Frameworks (COFs) of so far unprecedented flexibility in network topologies, far exceeding the conventional direct COF synthesis approach. However, to date only flexible cross-linkers were used in the MOF crosslinking approach, since a rigid cross-linker would require an ideal fit between the MOF structure and the cross-linker, which is experimentally extremely challenging, making in silico design mandatory. Here, we present an effective geometric method to find an ideal MOF cross-linker pair by employing a high-throughput screening approach. The algorithm considers distances, angles, and arbitrary rotations to optimally match the cross-linker inside the MOF structures. In a second, independent step, using Molecular Dynamics (MD) simulations we quantitatively confirmed all matches provided by the screening. Our approach thus provides a robust and powerful method to identify ideal MOF/Cross-linker combinations, which helped to identify several MOF-to-COF candidate structures by starting from suitable libraries. The algorithms presented here can be extended to other advanced network structures, such as mechanically interlocked materials or molecular weaving and knots.

Metal-Organic Frameworks and Metal-Organic Cages - A Perspective

The fields of metal-organic cages (MOCs) and metal-organic frameworks (MOFs) are both highly topical and continue to develop at a rapid pace. Despite clear synergies between the two fields, overlap is rarely observed. This article discusses the peculiarities and similarities of MOCs and MOFs in terms of synthetic strategies and approaches to system characterisation. The stability of both classes of material is compared, particularly in relation to their applications in guest storage and catalysis. Lastly, suggestions are made for opportunities for each field to learn and develop in partnership with the other.

Transfer Learning Study of Gas Adsorption in Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are a class of materials promising for gas adsorption due to their highly tunable nanoporous structures and host-guest interactions. While machine learning (ML) has been leveraged to aid the design or screen of MOFs for different purposes, the needs of big data are not always met, limiting the applicability of ML models trained against small data sets. In this work, we introduce an inductive transfer learning technique to improve the accuracy and applicability of ML models trained with a small amount of MOF adsorption data. This technique leverages potentially shareable knowledge from a source task to improve the models on the target tasks. As demonstrations, a deep neural networks (DNNs) trained on H₂ adsorption data with 13 506 MOF structures at 100 bar and 243 K is used as the source task. When transferring knowledge from the source task to H₂ adsorption at 100 bar and 130 K (one target task), the average predictive accuracy on target tasks was improved from 0.960 (direct training) to 0.991 (transfer learning), and transfer learning works in 89.3% of the cases. We also tested transfer learning across different gas species (i.e., from H₂ to CH₄), with an average predictive accuracy of CH₄ adsorption being improved from 0.935 (direct training) to 0.980 (transfer learning), and transfer learning works in 82.0% of the cases. More importantly, transfer learning is shown to effectively improve the models on the target tasks with low accuracy from direct training. However, when transferring the knowledge from the source task to Xe/Kr adsorption, the transfer learning does not improve the predictive accuracy significantly and even makes it worse in ∼50.0% of the cases, which is attributed to the lack of common descriptors that is key to the underlying knowledge.

Density Functionals for Hydrogen Storage: Defining the H2Bind275 Test Set with Ab Initio Benchmarks and Assessment of 55 Functionals

Efficient and high-capacity storage materials are indispensable for a hydrogen-based economy. In silico tools can accelerate the process of discovery of new adsorbent materials with optimal hydrogen adsorption enthalpies. Density functional theory is well-poised to become a very useful tool for enabling high-throughput screening of potential materials. In this work, we have identified density functional approximations that provide good performance for hydrogen binding applications following a two-pronged approach. First, we have compiled a data set (H2Bind275) that comprehensively represents the hydrogen binding problem capturing the chemical and mechanistic diversity in the binding sites encountered in hydrogen storage materials. We have also computed reference interaction energies for this data set using coupled-cluster theory. Second, we have assessed the performance of 55 density functional approximations for predicting H₂ interaction energies and have identified two hybrid density functionals (ωB97X-V and ωB97M-V), two double hybrid density functionals (DSD-PBEPBE-D3(BJ) and PBE0-DH), and one semilocal density functional (B97M-V) as the best performing ones. We have recommended the addition of empirical dispersion corrections to systematically underbinding density functionals such as revPBE, BLYP, and B3LYP for improvements in performance at negligible additional cost. We have also recommended the usage of the def2-TZVPP basis set as it represents a good compromise between accuracy and cost, limiting the finite basis set errors to less than 1 kJ/mol.

Electronic Structure Modeling of Metal-Organic Frameworks

Owing to their molecular building blocks, yet highly crystalline nature, metal-organic frameworks (MOFs) sit at the interface between molecule and material. Their diverse structures and compositions enable them to be useful materials as catalysts in heterogeneous reactions, electrical conductors in energy storage and transfer applications, chromophores in photoenabled chemical transformations, and beyond. In all cases, density functional theory (DFT) and higher-level methods for electronic structure determination provide valuable quantitative information about the electronic properties that underpin the functions of these frameworks. However, there are only two general modeling approaches in conventional electronic structure software packages: those that treat materials as extended, periodic solids, and those that treat materials as discrete molecules. Each approach has features and benefits; both have been widely employed to understand the emergent chemistry that arises from the formation of the metal-organic interface. This Review canvases these approaches to date, with emphasis placed on the application of electronic structure theory to explore reactivity and electron transfer using periodic, molecular, and embedded models. This includes (i) computational chemistry considerations such as how functional, k-grid, and other model variables are selected to enable insights into MOF properties, (ii) extended solid models that treat MOFs as materials rather than molecules, (iii) the mechanics of cluster extraction and subsequent chemistry enabled by these molecular models, (iv) catalytic studies using both solids and clusters thereof, and (v) embedded, mixed-method approaches, which simulate a fraction of the material using one level of theory and the remainder of the material using another dissimilar theoretical implementation.

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO2 reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand-ligand interactions. The classic example of Vaska's complex, trans-[Ir(PPh3)2(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)2(PPh3)2(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H2, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H2-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol-1, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol-1, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H2-activation barrier were identified.

Metal-Organic Framework Nanoparticles Induce Pyroptosis in Cells Controlled by the Extracellular pH

Ion homeostasis is essential for cellular survival, and elevated concentrations of specific ions are used to start distinct forms of programmed cell death. However, investigating the influence of certain ions on cells in a controlled way has been hampered due to the tight regulation of ion import by cells. Here, it is shown that lipid-coated iron-based metal-organic framework nanoparticles are able to deliver and release high amounts of iron ions into cells. While high concentrations of iron often trigger ferroptosis, here, the released iron induces pyroptosis, a form of cell death involving the immune system. The iron release occurs only in slightly acidic extracellular environments restricting cell death to cells in acidic microenvironments and allowing for external control. The release mechanism is based on endocytosis facilitated by the lipid-coating followed by degradation of the nanoparticle in the lysosome via cysteine-mediated reduction, which is enhanced in slightly acidic extracellular environment. Thus, a new functionality of hybrid nanoparticles is demonstrated, which uses their nanoarchitecture to facilitate controlled ion delivery into cells. Based on the selectivity for acidic microenvironments, the described nanoparticles may also be used for immunotherapy: the nanoparticles may directly affect the primary tumor and the induced pyroptosis activates the immune system.

Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO2 Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models

Postcombustion CO₂ capture and storage (CCS) is a key technological approach to reducing greenhouse gas emission while we transition to carbon-free energy production. However, current solvent-based CO₂ capture processes are considered too energetically expensive for widespread deployment. Vacuum swing adsorption (VSA) is a low-energy CCS that has the potential for industrial implementation if the right sorbents can be found. Metal-organic framework (MOF) materials are often promoted as sorbents for low-energy CCS by highlighting select adsorption properties without a clear understanding of how they perform in real-world VSA processes. In this work, atomistic simulations have been fully integrated with a detailed VSA simulator, validated at the pilot scale, to screen 1632 experimentally characterized MOFs. A total of 482 materials were found to meet the 95% CO₂ purity and 90% CO₂ recovery targets (95/90-PRTs)-365 of which have parasitic energies below that of solvent-based capture (∼290 kWh_e/MT CO₂) with a low value of 217 kWh_e/MT CO₂. Machine learning models were developed using common adsorption metrics to predict a material's ability to meet the 95/90-PRT with an overall prediction accuracy of 91%. It was found that accurate parasitic energy and productivity estimates of a VSA process require full process simulations.

A Robotics-Inspired Screening Algorithm for Molecular Caging Prediction

We define a molecular caging complex as a pair of molecules in which one molecule (the “host” or “cage”) possesses a cavity that can encapsulate the other molecule (the “guest”) and prevent it from escaping. Molecular caging complexes can be useful in applications such as molecular shape sorting, drug delivery, and molecular immobilization in materials science, to name just a few. However, the design and computational discovery of new caging complexes is a challenging task, as it is hard to predict whether one molecule can encapsulate another because their shapes can be quite complex. In this paper, we propose a computational screening method that predicts whether a given pair of molecules form a caging complex. Our method is based on a caging verification algorithm that was designed by our group for applications in robotic manipulation. We tested our algorithm on three pairs of molecules that were previously described in a pioneering work on molecular caging complexes and found that our results are fully consistent with the previously reported ones. Furthermore, we performed a screening experiment on a data set consisting of 46 hosts and four guests and used our algorithm to predict which pairs are likely to form caging complexes. Our method is computationally efficient and can be integrated into a screening pipeline to complement experimental techniques.

Revealing the potential application of chiral covalent organic frameworks in CO2 adsorption and separation

This work uses theoretical calculations to discover the potential application of CCOFs in gas adsorption and separation, and conduct theoretical research.

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.

Trends in Solid Adsorbent Materials Development for CO2 Capture

A recent report from the United Nations has warned about the excessive CO₂ emissions and the necessity of making efforts to keep the increase in global temperature below 2 °C. Current CO₂ capture technologies are inadequate for reaching that goal, and effective mitigation strategies must be pursued. In this work, we summarize trends in materials development for CO₂ adsorption with focus on recent studies. We put adsorbent materials into four main groups: (I) carbon-based materials, (II) silica/alumina/zeolites, (III) porous crystalline solids, and (IV) metal oxides. Trends in computational investigations along with experimental findings are covered to find promising candidates in light of practical challenges imposed by process economics.

Modeling and Simulation of Crystallization of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are the porous, crystalline structures made of metal–ligands and organic linkers that have applications in gas storage, gas separation, and catalysis. Several experimental and computational tools have been developed over the past decade to investigate the performance of MOFs for such applications. However, the experimental synthesis of MOFs is still empirical and requires trial and error to produce desired structures, which is due to a limited understanding of the mechanism and factors affecting the crystallization of MOFs. Here, we show for the first time a comprehensive kinetic model coupled with population balance model to elucidate the mechanism of MOF synthesis and to estimate size distribution of MOFs growing in a solution of metal–ligand and organic linker. The oligomerization reactions involving metal–ligand and organic linker produce secondary building units (SBUs), which then aggregate slowly to yield MOFs. The formation of secondary building units (SBUs) and their evolution into MOFs are modeled using detailed kinetic rate equations and population balance equations, respectively. The effect of rate constants, aggregation frequency, the concentration of organic linkers, and concurrent crystallization of organic linkers are studied on the dynamics of SBU and MOF formation. The results qualitatively explain the longer timescales involved in the synthesis of MOF. The fundamental insights gained from modeling and simulation analysis can be used to optimize the operating conditions for a higher yield of MOF crystals.

Computational prediction of promising pyrazine and bipyridine analogues of a fluorinated MOF platform, MFN-Ni-L (M = SI/AL; N = SIX/FIVE; L = pyr/bipyr), for CO2 capture under pre-humidified conditions

The CO2 separation performance has been explored under dry and pre-humidified conditions for the fluorinated metal organic frameworks SIFSIX-Ni-pyr, ALFFIVE-Ni-pyr, SIFSIX-Ni-bipyr and ALFFIVE-Ni-bipyr, which are incorporated with/without a coordinatively unsaturated site (CUS), Al, and organic linkers 1,4-pyrazine and 4,4'-bipyridine. The ultra-small pore size (∼4 Å) of the pyrazine analogues of fluorinated-MOFs, i.e. SIFSIX-Ni-pyr and ALFFIVE-Ni-pyr, showed infinite selectivity towards CO2 with low uptake capability under dry conditions, which drastically reduced to 0 wt% under very low pre-humidification conditions (∼10-15 wt% adsorbed moisture). In marked contrast, the bipyridine analogues SIFSIX-Ni-bipyr and ALFFIVE-Ni-bipyr showed significant CO2 capture performance of up to 30-40 wt% with pre-humidification. Further, both SIFSIX-Ni-bipyr and ALFFIVE-Ni-bipyr showed high CO2 selectivity as well as good working capacity at 1-10 bar while poor selectivity is observed for the pyrazine analogues with pre-humidification. The incorporation of Al as a CUS provides stability to the ALFFIVE-Ni-bipyr fluorinated-MOF under pre-humidified conditions due to the ability of Al to co-ordinate with water molecules in hydrogen bonded networks. Therefore, to develop physisorption-based CO2 capturing processes under pre-humidified conditions, the use of Al incorporated ALFFIVE-Ni-bipyr is highly suitable, which may outperform most of the fluorinated-MOFs and other classes of porous solids reported so far.

Modelling of framework materials at multiple scales: current practices and open questions

The last decade has seen an explosion of the family of framework materials and their study, from both the experimental and computational points of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of material databases and the growing use of machine learning for the prediction of properties. This article is part of the theme issue 'Mineralomimesis: natural and synthetic frameworks in science and technology'.

Drug-Membrane Permeability across Chemical Space

Unraveling the relation between the chemical structure of small druglike compounds and their rate of passive permeation across lipid membranes is of fundamental importance for pharmaceutical applications. The elucidation of a comprehensive structure-permeability relationship expressed in terms of a few molecular descriptors is unfortunately hampered by the overwhelming number of possible compounds. In this work, we reduce a priori the size and diversity of chemical space to solve an analogous-but smoothed out-structure-property relationship problem. This is achieved by relying on a physics-based coarse-grained model that reduces the size of chemical space, enabling a comprehensive exploration of this space with greatly reduced computational cost. We perform high-throughput coarse-grained (HTCG) simulations to derive a permeability surface in terms of two simple molecular descriptors-bulk partitioning free energy and pK a. The surface is constructed by exhaustively simulating all coarse-grained compounds that are representative of small organic molecules (ranging from 30 to 160 Da) in a high-throughput scheme. We provide results for acidic, basic, and zwitterionic compounds. Connecting back to the atomic resolution, the HTCG predictions for more than 500 000 compounds allow us to establish a clear connection between specific chemical groups and the resulting permeability coefficient, enabling for the first time an inverse design procedure. Our results have profound implications for drug synthesis: the predominance of commonly employed chemical moieties narrows down the range of permeabilities.