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

Computational Modeling of Reticular Materials: The Past, the Present, and the Future

Reticular materials rely on a unique building concept where inorganic and organic building units are stitched together giving access to an almost limitless number of structured ordered porous materials. Given the versatility of chemical elements, underlying nets, and topologies, reticular materials provide a unique platform to design materials for timely technological applications. Reticular materials have now found their way in important societal applications, like carbon capture to address climate change, water harvesting to extract atmospheric moisture in arid environments, and clean energy applications. Combining predictions from computational materials chemistry with advanced experimental characterization and synthesis procedures unlocks a design strategy to synthesize new materials with the desired properties and functions. Within this review, the current status of modeling reticular materials is addressed and supplemented with topical examples highlighting the necessity of advanced molecular modeling to design materials for technological applications. This review is structured as a templated molecular modeling study starting from the molecular structure of a realistic material towards the prediction of properties and functions of the materials. At the end, the authors provide their perspective on the past, present of future in modeling reticular materials and formulate open challenges to inspire future model and method developments.

Computational Exploration of Adsorption-Based Hydrogen Storage in Mg-Alkoxide Functionalized Covalent-Organic Frameworks (COFs): Force-Field and Machine Learning Models

Hydrogen is a clean-burning fuel that can be converted to other forms. of energy without generating any greenhouse gases. Currently, hydrogen is stored either by compression to high pressure (>700 bar) or cryogenic cooling to liquid form (<23 K). Therefore, it is essential to develop safe, reliable, and energy-efficient storage technology that can store hydrogen at lower pressures and temperatures. In this work, we systematically designed 2902 Mg-alkoxide-functionalized covalent-organic frameworks (COFs) and performed high-throughput (HT) computational screening for hydrogen storage applications at 111, 231, and 296 K. To accurately model the interaction between Mg-alkoxide sites and molecular hydrogen, we performed MP2 calculations to compute the hydrogen binding energy for different types of functionalized models, and the data were subsequently used to fit modified-Morse force field (FF) parameters. Using the developed FF models, we conducted HT grand canonical Monte Carlo (GCMC) simulations to compute hydrogen uptakes for both original and functionalized COFs. The generated data were subsequently used to evaluate the materials' gravimetric and volumetric storage performance at various temperatures (111, 231, and 296 K). Finally, we developed machine learning (ML) models to predict the hydrogen storage performance of functionalized structures based on the features of the original structures. The developed model showed excellent performance with a mean absolute error (MAE) of 0.061 wt % and 0.456 g/L for predicting the gravimetric and volumetric deliverable capacities, enabling a quick evaluation of structures in a hypothetical COF database. The screening results demonstrated that the Mg-alkoxide functionalization yields greater improvements in volumetric H2 storage capacities for COFs with smaller pores compared to those with larger (mesoporous) pores.

pyMSER─An Open-Source Library for Automatic Equilibration Detection in Molecular Simulations

Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depends on simulation conditions, predicting the optimal number of simulation steps a priori is impossible. Here, we demonstrate the application of the Marginal Standard Error Rule (MSER) for automatically identifying the optimal truncation point in Grand Canonical Monte Carlo (GCMC) simulations. This novel automatic procedure determines the point at which a steady state is reached, ensuring that figures of merit are extracted in an objective, accurate, and reproducible fashion. In the case of GCMC simulations of gas adsorption in metal-organic frameworks, we find that this methodology reduces the computational cost by up to 90%. As MSER statistics are independent of the simulation method that creates the data, this library is, in principle, applicable to any time series analysis in which equilibration truncation is required. The open-source Python implementation of our method, pyMSER, is publicly available for reuse and validation at https://github.com/IBM/pymser.

Transitioning metal-organic frameworks from the laboratory to market through applied research

Metal-organic frameworks (MOFs) have captivated researchers for over 25 years, yet few have successfully transitioned to commercial markets. This Perspective elucidates the progress, challenges and opportunities in moving MOFs to market, focusing on applied research. The five applied research steps that enable technology development and demonstration are reviewed: synthesis, forming, processing (washing and activation), prototyping and compliance. Furthermore, the importance of a comprehensive techno-economic analysis incorporating a complete picture of costs and revenues is discussed. Readers can use the understanding of applied research presented herein to tackle their MOF commercialization challenges.

Metal-Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set

Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity of water. Consequently, it is useful to predict whether a MOF is water-stable before investing time and resources into synthesis. Existing heuristics for designing water-stable MOFs lack generality and limit the diversity of explored chemistry due to narrowly defined criteria. Machine learning (ML) models offer the promise to improve the generality of predictions but require data. In an improvement on previous efforts, we enlarge the available training data for MOF water stability prediction by over 400%, adding 911 MOFs with water stability labels assigned through semiautomated manuscript analysis to curate the new data set WS24. The additional data are shown to improve ML model performance (test ROC-AUC > 0.8) over diverse chemistry for the prediction of both water stability and stability in harsher acidic conditions. We illustrate how the expanded data set and models can be used with a previously developed activation stability model in combination with genetic algorithms to quickly screen ∼10,000 MOFs from a space of hundreds of thousands for candidates with multivariate stability (upon activation, in water, and in acid). We uncover metal- and geometry-specific design rules for robust MOFs. The data set and ML models developed in this work, which we disseminate through an easy-to-use web interface, are expected to contribute toward the accelerated discovery of novel, water-stable MOFs for applications such as direct air gas capture and water treatment.

Deep learning-based recommendation system for metal-organic frameworks (MOFs)

This work presents a recommendation system for metal-organic frameworks (MOFs) inspired by online content platforms. By leveraging the unsupervised Doc2Vec model trained on document-structured intrinsic MOF characteristics, the model embeds MOFs into a high-dimensional chemical space and suggests a pool of promising materials for specific applications based on user-endorsed MOFs with similarity analysis. This proposed approach significantly reduces the need for exhaustive labeling of every material in the database, focusing instead on a select fraction for in-depth investigation. Ranging from methane storage and carbon capture to quantum properties, this study illustrates the system's adaptability to various applications.

PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks

We report a fast and easy method (PACMAN) to assign partial atomic charges on metal-organic framework (MOF) and covalent-organic framework (COF) crystal structures based on graph convolution networks (GCNs) trained on >1.8 million high-fidelity partial atomic charge data obtained from the Quantum Metal-Organic Framework (QMOF) database. The developed model shows outstanding performance, achieving a mean absolute error (MAE) of 0.0055 e (test set performance) while maintaining consistency with DDEC6, Bader, and CM5 charges across diverse chemistry and topologies of MOFs and COFs. We find that the new method accurately assigns partial atomic charges for ion-containing nanoporous materials, which has not been possible in previous machine learning (ML) models. Grand canonical Monte Carlo (GCMC) simulation results for CO2 and N2 uptakes and the Widom particle insertion calculation for Henry's law constant of water results based on PACMAN and the original DDEC6 charges show excellent agreements compared to other ML models reported in the literature. The runtime analysis of the new method demonstrates that the partial atomic charges of MOF and COF structures with up to 500 atoms can be obtained in less than 10 s. An easy-to-use web interface has been developed to facilitate the adoption of the developed model.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

Covalent organic frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry, making them an ideal choice for a wide range of applications. However, exploring COFs experimentally can be arduous and time-consuming due to their immense number of potential structures. As a result, computational high-throughput studies have become an attractive option. Nevertheless, generating COF structures can also be a challenging and time-consuming task. To address this challenge, here, we introduce the pyCOFBuilder, an open-source Python package designed to facilitate the generation of COF structures for computational studies. The pyCOFBuilder software provides an easy-to-use set of functionalities to generate COF structures following the reticular approach. In this paper, we describe the implementation, main features, and capabilities of the pyCOFBuilder, demonstrating its utility for generating COF structures with varying topologies and chemical properties. pyCOFBuilder is freely available on GitHub at https://github.com/lipelopesoliveira/pyCOFBuilder.

From nano- to macroarchitectures: designing and constructing MOF-derived porous materials for persulfate-based advanced oxidation processes

Persulfate-based advanced oxidation processes (PS-AOPs) have gained significant attention as an effective approach for the elimination of emerging organic contaminants (EOCs) in water treatment. Metal-organic frameworks (MOFs) and their derivatives are regarded as promising catalysts for activating peroxydisulfate (PDS) and peroxymonosulfate (PMS) due to their tunable and diverse structure and composition. By the rational nanoarchitectured design of MOF-derived nanomaterials, the excellent performance and customized functions can be achieved. However, the intrinsic fine powder form and agglomeration ability of MOF-derived nanomaterials have limited their practical engineering application. Recently, a great deal of effort has been put into shaping MOFs into macroscopic objects without sacrificing the performance. This review presents recent advances in the design and synthetic strategies of MOF-derived nano- and macroarchitectures for PS-AOPs to degrade EOCs. Firstly, the strategies of preparing MOF-derived diverse nanoarchitectures including hierarchically porous, hollow, yolk-shell, and multi-shell structures are comprehensively summarized. Subsequently, the approaches of manufacturing MOF-based macroarchitectures are introduced in detail. Moreover, the PS-AOP application and mechanisms of MOF-derived nano- and macromaterials as catalysts to eliminate EOCs are discussed. Finally, the prospects and challenges of MOF-derived materials in PS-AOPs are discussed. This work will hopefully guide the design and development of MOF-derived porous materials in SR-AOPs.

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics

In this work, we introduced COFInformatics, a computational approach merging molecular simulations and machine learning (ML) algorithms, to evaluate all synthesized and hypothetical covalent organic frameworks (COFs) for the CO2/CH4 mixture separation under four different adsorption-based processes: pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), and pressure-temperature swing adsorption (PTSA). We first extracted structural, chemical, energy-based, and graph-based molecular fingerprint features of every single COF structure in the very large COF space, consisting of nearly 70,000 materials, and then performed grand canonical Monte Carlo simulations to calculate the CO2/CH4 mixture adsorption properties of 7540 COFs. These features and simulation results were used to develop ML models that accurately and rapidly predict CO2/CH4 mixture adsorption and separation properties of all 68,614 COFs. The most efficient separation process and the best adsorbent candidates among the entire COF spectrum were identified and analyzed in detail to reveal the most important molecular features that lead to high-performance adsorbents. Our results showed that (i) many hypoCOFs outperform synthesized COFs by achieving higher CO2/CH4 selectivities; (ii) the top COF adsorbents consist of narrow pores and linkers comprising aromatic, triazine, and halogen groups; and (iii) PTSA is the most efficient process to use COF adsorbents for natural gas purification. We believe that COFInformatics promises to expedite the evaluation of COF adsorbents for CO2/CH4 separation, thereby circumventing the extensive, time- and resource-intensive molecular simulations.

Accelerating In Silico Discovery of Metal-Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning

Cryogenic distillation, a currently employed method for C2H4/C2H6 and C3H6/C3H8 mixture separation, is energy-intensive, prompting the research toward alternative technologies, including adsorbent-based separation. In this work, we combine machine learning (ML) technique with high-throughput screening to screen ∼23,000 hypothetical metal-organic frameworks (MOFs) for paraffin (C2H6 and C3H8) selective adsorbent separation. First, structure-based prescreening was employed to remove MOFs with undesired geometric properties. Further, a random forest model built upon the multicomponent grand canonical Monte Carlo (m-GCMC) simulation data of training set MOFs was found to be the most successful in learning the relationship between MOF features and olefin/paraffin mixture separation. Using this technique, the separation performance of the remaining (test set) MOFs was predicted, and the top-performing MOFs were identified. We also employed active learning (AL) to evaluate its effectiveness in improving the prediction of olefin/paraffin selectivity. AL was discovered to be ∼29 times more efficient than the best-supervised ML model, as it was able to identify the top materials in limited training data and at a fraction of computational cost and time as compared to ML techniques. Among the top selected materials, framework chemistry was found to be the most important parameter. Nickel and copper (as a metal node) in a tfzd and hms topological arrangement respectively, were discovered to be a prevalent attribute in high-performing MOFs, further demonstrating the prominent significance of framework chemistry. Additionally, the top MOFs discovered were studied in detail and further compared to the previously reported MOFs. These MOFs show the highest selectivity for C2H4/C2H6 and C3H6/C3H8 mixture separation, as reported until date. The hierarchical strategy devised in this study will facilitate the quick screening of MOFs across multiple databases toward industrially significant separation processes by leveraging molecular simulations and AL.

Identifying pathways to metal-organic framework collapse during solvent activation with molecular simulations

Metal-organic framework (MOF) materials are a vast family of nanoporous solids with potential applications ranging from drug delivery to environmental remediation. Application of MOFs in these scenarios is hindered, however, by difficulties in MOF 'activation' after initial synthesis - removal of the synthesis solvent from the pores to make the pore space accessible - often leading to framework collapse if improperly performed. While experimental studies have correlated collapse to specific solvent properties and conditions, the mechanism of activation-collapse is currently unknown. Developing this understanding would enable researchers to create better activation protocols for MOFs, accelerating discovery and process intensification. To achieve this goal, we simulated solvent removal using grand-canonical Monte Carlo and free energy perturbation methods. By framing activation as a fluid desorption problem, we investigated activation processes in the isoreticular metal organic framework (IRMOF) family of MOFs for different solvents. We identified two pathways for solvent activation - the solvent either desorbs uniformly from each individual pore or forms coexisting phases during desorption. These mesophases in turn lead to large capillary stresses within the framework, corroborating experimental hypotheses for the cause of activation-collapse. Finally, we found that the activation energy of solvent removal increased with pore size and connectivity due to the increased stability of solvent mesophases, matching experimental findings. Using these simulations, it is possible to screen MOF activation procedures, enabling rapid identification of ideal solvents and conditions and thus enabling faster development of MOFs for practical applications.

Self-supported MOF/cellulose-nanocrystals materials designed from ultrafiltration

Metal-organic-frameworks (MOFs) are promising materials for addressing critical issues such as petrochemical separation, water purification, energy storage and drug delivery. Their large-scale deployment, however, is hampered by a limited processability due to their powdery nature. Recently, the hybridization of MOFs with biopolymers has emerged as a greener, biocompatible strategy to shape MOFs composites into more processable membranes, films, and porous materials. In this work, cellulose nanocrystals (CNCs) were used in combination with ZIF-8 (a widely used synthetic zeolite) to produce hybrid composites through ultrafiltration. Results showed that small quantities of CNCs (1 to 20 CNC:ZIF-8 volume ratio) were sufficient to form a self-supported, dense deposit with high ZIF-8 loadings. Compared to classical MOF in situ growth strategies, this approach allowed the tuning of the composition of the final nanocomposite by controlling the nature and quantities of particles in the suspension. The fabrication of the deposit was strongly dependent on the physiochemical properties of the suspension, which were fully characterized with a set of complementary techniques, including in situ SAXS. This technique was employed to investigate the filtration process, which exhibited a homogeneous deposition of ZIF-8 particles mediated by CNC self-assembly. Finally, the available pore volume and integrity of the internal porosity of ZIF-8 were characterized by water porosimetry, demonstrating that the presence of CNCs did not alter the properties of the supported ZIF-8.

MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks

Metal-organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs.Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility-a MOF key performance indicator-of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host-guest interaction studies.

Taming Multiscale Structural Complexity in Porous Skeletons: From Open Framework Materials to Micro/Nanoscaffold Architectures

Recent developments in the design and synthesis of more and more sophisticated organic building blocks with controlled structures and physical properties, combined with the emergence of novel assembly modes and nanofabrication methods, make it possible to tailor unprecedented structurally complex porous systems with precise multiscale control over their architectures and functions. By tuning their porosity from the nanoscale to microscale, a wide range of functional materials can be assembled, including open frameworks and micro/nanoscaffold architectures. During the last two decades, significant progress is made on the generation and optimization of advanced porous systems, resulting in high-performance multifunctional scaffold materials and novel device configurations. In this perspective, a critical analysis is provided of the most effective methods for imparting controlled physical and chemical properties to multifunctional porous skeletons. The future research directions that underscore the role of skeleton structures with varying physical dimensions, from molecular-level open frameworks (<10 nm) to supramolecular scaffolds (10-100 nm) and micro/nano scaffolds (>100 nm), are discussed. The limitations, challenges, and opportunities for potential applications of these multifunctional and multidimensional material systems are also evaluated in particular by addressing the greatest challenges that the society has to face.

Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Porous organic frameworks (POFs) have become a highly sought-after research domain that offers a promising avenue for developing cutting-edge nanostructured materials, both in their pristine state and when subjected to various chemical and structural modifications. Metal-organic frameworks, covalent organic frameworks, and hydrogen-bonded organic frameworks are examples of these emerging materials that have gained significant attention due to their unique properties, such as high crystallinity, intrinsic porosity, unique structural regularity, diverse functionality, design flexibility, and outstanding stability. This review provides an overview of the state-of-the-art research on base-stable POFs, emphasizing the distinct pros and cons of reticular framework nanoparticles compared to other types of nanocluster materials. Thereafter, the review highlights the unique opportunity to produce multifunctional tailoring nanoparticles to meet specific application requirements. It is recommended that this potential for creating customized nanoparticles should be the driving force behind future synthesis efforts to tap the full potential of this multifaceted material category.

MFM-300(Sc): a chemically stable Sc(III)-based MOF material for multiple applications

Developing robust multifunctional metal-organic frameworks (MOFs) is the key to advancing the further deployment of MOFs into relevant applications. Since the first report of MFM-300(Sc) (MFM = Manchester Framework Material, formerly known as NOTT-400), the development of applications of this robust microporous MOF has only grown. In this review, a summary of the applications of MFM-300(Sc), as well as some emerging advanced applications, have been discussed. The adsorption properties of MFM-300(Sc) are presented systematically. Particularly, this contribution is focused on acid and corrosive gas adsorption. In addition, recent applications for catalysis based on the outstanding hemilabile Sc-O bond character are highlighted. Finally, some new research areas are introduced, such as host-guest chemistry and biomedical applications. This highlight aims to showcase the recent advances and the potential for developing new applications of this promising material.

Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learning

A high-throughput computational screening approach combined with machine learning (ML) was introduced to unlock the potential of both synthesized and hypothetical COFs (hypoCOFs) for adsorption-based CH₄/H₂ separation. We studied 597 synthesized COFs for adsorption of a CH₄/H₂ mixture using Grand Canonical Monte Carlo (GCMC) simulations under pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) conditions. Based on the simulation results, the CH₄/H₂ selectivities, CH₄ working capacities, adsorbent performance scores, and regenerabilities of the synthesized COFs were assessed and the structural properties of the top-performing COFs were identified. The hypoCOF database composed of 69 840 materials was then filtered to identify 7737 hypothetical materials having similar structural properties to the top synthesized COFs. These hypothetical COFs were then examined for CH₄/H₂ separation using molecular simulations and the results showed that the top hypoCOFs have CH₄ selectivities and working capacities in the ranges of 21.9-28.7 (64.7-128.6) and 5.8-7.6 (1.3-3.1) mol kg^-1 under PSA (VSA) conditions, respectively, outperforming the synthesized COFs and metal-organic frameworks (MOFs). ML models were then developed based on the hypoCOF simulation results to accurately predict the CH₄/H₂ mixture adsorption properties of all remaining hypothetical materials when their structural and chemical properties are fed into the models. These models accurately assessed the CH₄/H₂ mixture separation performances of any hypoCOF within seconds without performing computationally demanding molecular simulations. The computational approach that we have proposed in this study will provide an accurate and efficient assessment of COF materials for CH₄/H₂ separation and significantly accelerate the experimental efforts towards the design and discovery of new high-performing COF adsorbents.

Fast Dynamic Synthesis of MIL-68(In) Thin Films in High Optical Quality for Optical Cavity Sensing

Fabrication of metal-organic framework (MOF) thin films rigidly anchored on suitable substrates is a crucial prerequisite for the integration of these porous hybrid materials into electronic and optical devices. Thus, far, the structural variety for MOF thin films available through layer-by-layer deposition was limited, as the preparation of those surface-anchored metal-organic frameworks (SURMOFs) has several requirements: mild conditions, low temperatures, day-long reaction times, and nonaggressive solvents. We herein present a fast method for the preparation of the MIL SURMOF on Au-surfaces under rather harsh conditions: Using a dynamic layer-by-layer synthesis for MIL-68(In), thin films of adjustable thickness between 50 and 2000 nm could be deposited within only 60 min. The MIL-68(In) thin film growth was monitored in situ using a quartz crystal microbalance. In-plane X-ray diffraction revealed oriented MIL-68(In) growth with the pore-channels of this interesting MOF aligned parallel to the support. Scanning electron microscopy data demonstrated an extraordinarily low roughness of the MIL-68(In) thin films. Mechanical properties and lateral homogeneity of the layer were probed through nanoindentation. These thin films showed extremely high optical quality. By applying a poly(methyl methacrylate) layer and further depositing an Au-mirror to the top, a MOF optical cavity was fabricated that can be used as a Fabry-Perot interferometer. The MIL-68(In)-based cavity showed a series of sharp resonances in the ultraviolet-visible regime. Changes in the refractive index of MIL-68(In) caused by exposure to volatile compounds led to pronounced position shifts of the resonances. Thus, these cavities are well suited to be used as optical read-out sensors.

Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74

Among various porous solids for gas separation and purification, metal-organic frameworks (MOFs) are promising materials that potentially combine high CO₂ uptake and CO₂/N₂ selectivity. So far, within the hundreds of thousands of MOF structures known today, it remains a challenge to computationally identify the best suited species. First principle-based simulations of CO₂ adsorption in MOFs would provide the necessary accuracy; however, they are impractical due to the high computational cost. Classical force field-based simulations would be computationally feasible; however, they do not provide sufficient accuracy. Thus, the entropy contribution that requires both accurate force fields and sufficiently long computing time for sampling is difficult to obtain in simulations. Here, we report quantum-informed machine-learning force fields (QMLFFs) for atomistic simulations of CO₂ in MOFs. We demonstrate that the method has a much higher computational efficiency (∼1000×) than the first-principle one while maintaining the quantum-level accuracy. As a proof of concept, we show that the QMLFF-based molecular dynamics simulations of CO₂ in Mg-MOF-74 can predict the binding free energy landscape and the diffusion coefficient close to experimental values. The combination of machine learning and atomistic simulation helps achieve more accurate and efficient in silico evaluations of the chemisorption and diffusion of gas molecules in MOFs.

General Strategy for Incorporation of Functional Group Handles into Covalent Organic Frameworks via the Ugi Reaction

The library of imine-linked covalent organic frameworks (COFs) has grown significantly over the last two decades, featuring a variety of morphologies, pore sizes, and applications. An array of synthetic methods has been developed to expand the scope of the COF functionalities; however, most of these methods were designed to introduce functional scaffolds tailored to a specific application. Having a general approach to diversify COFs via late-stage incorporation of functional group handles would greatly facilitate the transformation of these materials into platforms for a variety of useful applications. Herein, we report a general strategy to introduce functional group handles in COFs via the Ugi multicomponent reaction. To demonstrate the versatility of this approach, we have synthesized two COFs with hexagonal and kagome morphologies. We then introduced azide, alkyne, and vinyl functional groups, which could be readily utilized for a variety of post-synthetic modifications. This facile approach enables the functionalization of any COFs containing imine linkages.

Combining microfluidic paper-based platform and metal-organic frameworks in a single device for phenolic content assessment in fruits

A microfluidic paper-based device (µPAD) has been combined with metal-organic frameworks (MOFs) for total phenolic compounds (TPC) quantification in fruit samples for the first time. The performance of the µPAD, based upon the vertical flow approach, was enhanced in order to determine the TPC content with high accuracy in fruit samples. The method was based on the traditional Folin-Ciocalteu Index using gallic acid or oenotannin as reference phenolic compounds. This novel design and construction of the device are in agreement with the principles of Green Chemistry avoiding wax technology (lower toxicity). The analytical parameters that affect the colorimetric method (using digital imaging of the colored zone) performance were optimized including design, sample volume, and MOF amount. Then, the analytical features of the developed method were investigated such as dynamic range (1.6-30 mg L^-1), limit of detection (0.5 mg L^-1), and precision (RSD < 9%). Besides, the in-field analysis is achievable with a color stability up to 6 h after the loading process of the sample and storage stability for at least 15 days without performance losses (under vacuum at - 20 °C). Furthermore, the MOF ZIF-8@paper was characterized to study its composition and the successful combination. The feasibility of the proposed method was demonstrated by determining the TPC in 5 fruit samples using oenotannin as reference solute. The accuracy was validated by comparison of the data with the results obtained with the recommended protocol proposed by the International Organisation of Vine and Wine (OIV).

Nucleation of zeolitic imidazolate frameworks: from molecules to nanoparticles

We have studied the clusters involved in the initial stages of nucleation of Zeolitic Imidazolate Frameworks, employing a wide range of computational techniques. In the pre-nucleating solution, the prevalent cluster is the ZnIm₄ cluster (formed by a zinc cation, Zn²⁺, and four imidazolate anions, Im^-), although clusters such as ZnIm₃, Zn₂Im₇, Zn₂Im₇, Zn₃Im₉, Zn₃Im₁₀, or Zn₄Im₁₂ have energies that are not much higher, so they would also be present in solution at appreciable quantities. All these species, except ZnIm₃, have a tetrahedrally coordinated Zn²⁺ cation. Small Zn_xIm_y clusters are less stable than the ZnIm₄ cluster. The first cluster that is found to be more stable than ZnIm₄ is the Zn₄₁Im₈₈ cluster, which is a disordered cluster with glassy structure. Bulk-like clusters do not begin to be more stable than glassy clusters until much larger sizes, since the larger cluster we have studied (Zn₁₄₄Im₂₈₈) is still less stable than the glassy Zn₄₁Im₈₈ cluster, suggesting that Ostwald's rule (the less stable polymorph crystallizes first) could be fulfilled, not for kinetic, but for thermodynamic reasons. Our results suggest that the first clusters formed in the nucleation process would be glassy clusters, which then undergo transformation to any of the various crystal structures possible, depending on the kinetic routes provided by the synthesis conditions. Our study helps elucidate the way in which the various species present in solution interact, leading to nucleation and crystal growth.

Hypoxia-responsive covalent organic framework by single NIR laser-triggered for multimodal synergistic therapy of triple-negative breast cancer

In recent years, laser-mediated photodynamic therapy and photothermal therapy have attracted widespread attention due to their minimally invasive, easy to operate characteristics and high specificity. However, the traditional photodynamic or photothermal therapy exist several shortcomings such as the hypoxic microenvironment, intracellular heat shock proteins or complex operation. In this study, covalent organic framework (COF) was used as the drug carrier to equip with the photosensitizer indocyanine green (ICG) and the hypoxia-activating prodrug AQ4N. The hyaluronic acid (HA) was modified on the surface of COF to obtain the HA-COF@ICG/AQ4N drug delivery system. HA-modified COF delivery systems can target tumor cells through recognize CD44 which is overexpressed in the surface of tumor cells membrane. Under the irradiation of single NIR laser, ICG that can excite the nanoplatform simultaneously produces a combined effect of photodynamic and photothermal. At the same time, photodynamic therapy through depleting intracellular oxygen exacerbates the hypoxic state of the tumor microenvironment, which in turn enhances AQ4N reduced to chemotherapeutic drug AQ4, producing a synergistic cascade antitumor effect. The results of our study by tumor cell and tumor spheroids indicated that the hypoxia-activated multi-functional nanoplatform could effectively inhibit the growth and metastasis of triple-negative breast cancer.

Graphene-Based Metal-Organic Framework Hybrids for Applications in Catalysis, Environmental, and Energy Technologies

Current energy and environmental challenges demand the development and design of multifunctional porous materials with tunable properties for catalysis, water purification, and energy conversion and storage. Because of their amenability to de novo reticular chemistry, metal-organic frameworks (MOFs) have become key materials in this area. However, their usefulness is often limited by low chemical stability, conductivity and inappropriate pore sizes. Conductive two-dimensional (2D) materials with robust structural skeletons and/or functionalized surfaces can form stabilizing interactions with MOF components, enabling the fabrication of MOF nanocomposites with tunable pore characteristics. Graphene and its functional derivatives are the largest class of 2D materials and possess remarkable compositional versatility, structural diversity, and controllable surface chemistry. Here, we critically review current knowledge concerning the growth, structure, and properties of graphene derivatives, MOFs, and their graphene@MOF composites as well as the associated structure-property-performance relationships. Synthetic strategies for preparing graphene@MOF composites and tuning their properties are also comprehensively reviewed together with their applications in gas storage/separation, water purification, catalysis (organo-, electro-, and photocatalysis), and electrochemical energy storage and conversion. Current challenges in the development of graphene@MOF hybrids and their practical applications are addressed, revealing areas for future investigation. We hope that this review will inspire further exploration of new graphene@MOF hybrids for energy, electronic, biomedical, and photocatalysis applications as well as studies on previously unreported properties of known hybrids to reveal potential "diamonds in the rough".

MOF/COF hybrids as next generation materials for energy and biomedical applications

The rapid increase in the number and variety of metal organic frameworks (MOFs) and covalent organic frameworks (COFs) has led to groundbreaking applications in the field of materials science and engineering. New MOF/COF hybrids combine the outstanding features of MOF and COF structures, such as high crystallinities, large surface areas, high porosities, the ability to decorate the structures with functional groups, and improved chemical and mechanical stabilities. These new hybrid materials offer promising performances for a wide range of applications including catalysis, energy storage, gas separation, and nanomedicine. In this highlight, we discuss the recent advancements of MOF/COF hybrids as next generation materials for energy and biomedical applications with a special focus on the use of computational tools to address the opportunities and challenges of using MOF/COF hybrids for various applications.

Materials discovery of ion-selective membranes using artificial intelligence

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.

Temperature Extrapolation of Henry's Law Constants and the Isosteric Heat of Adsorption

Computational screening of adsorbent materials often uses the Henry's law constant (K_H) (at a particular temperature) as a first discriminator metric due to its relative ease of calculation. The isosteric heat of adsorption in the limit of zero pressure (q_st^∞) is often calculated along with the Henry's law constant, and both properties are informative metrics of adsorbent material performance at low-pressure conditions. In this article, we introduce a method for extrapolating K_H as a function of temperature, using series-expansion coefficients that are easily computed at the same time as K_H itself; the extrapolation function also yields q_st^∞. The extrapolation is highly accurate over a wide range of temperatures when the basis temperature is sufficiently high, for a wide range of adsorbent materials and adsorbate gases. Various results suggest that the extrapolation is accurate when the extrapolation range in inverse-temperature space is limited to |β - β₀ | < 0.5 mol/kJ. Application of the extrapolation to a large set of materials is shown to be successful provided that K_H is not extremely large and/or the extrapolation coefficients converge satisfactorily. The extrapolation is also able to predict q_st^∞ for a system that shows an unusually large temperature dependence. The work provides a robust method for predicting K_H and q_st^∞ over a wide range of industrially relevant temperatures with minimal effort beyond that necessary to compute those properties at a single temperature, which facilitates the addition of practical operating (or processing) conditions to computational screening exercises.

Excited-State Properties for Extended Systems: Efficient Hybrid Density Functional Methods

Time-dependent density functional theory has become state-of-the-art for describing photophysical and photochemical processes in extended materials because of its affordable cost. The inclusion of exact exchange was shown to be essential for the correct description of the long-range asymptotics of electronic interactions and thus a well-balanced description of valence, Rydberg, and charge-transfer excitations. Several approaches for an efficient treatment of exact exchange have been established for the ground state, while implementations for excited-state properties are rare. Furthermore, the high computational costs required for excited-state properties in comparison to ground-state computations often hinder large-scale applications on periodic systems with hybrid functional accuracy. We therefore propose two approximate schemes for improving computational efficiency for the treatment of exact exchange. Within the auxiliary density matrix method (ADMM), exact exchange is estimated using a relatively small auxiliary basis and the introduced basis set incompleteness error is compensated by an exchange density functional correction term. Benchmark results for a test set of 35 molecules demonstrate that the mean absolute error introduced by ADMM is smaller than 0.3 pm for excited-state bond lengths and in the range of 0.02-0.04 eV for vertical excitation, adiabatic excitation, and fluorescence energies. Computational timings for a series of covalent-organic frameworks demonstrate that a speed-up of at least 1 order of magnitude can be achieved for excited-state geometry optimizations in comparison to conventional hybrid functionals. The second method is to use a semiempirical tight binding approximation for both Coulomb and exchange contributions to the excited-state kernel. This simplified Tamm-Dancoff approximation (sTDA) achieves an accuracy comparable to approximated hybrid density functional theory when referring to highly accurate coupled-cluster reference data. We find that excited-state bond lengths deviate by 1.1 pm on average and mean absolute errors in vertical excitation, adiabatic excitation, and fluorescence energies are in the range of 0.2-0.5 eV. In comparison to ADMM-approximated hybrid functional theory, sTDA accelerates the computation of broad-band excitation spectra by 1 order of magnitude, suggesting its potential use for large-scale screening purposes.

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.

Parametrization of Nonbonded Force Field Terms for Metal-Organic Frameworks Using Machine Learning Approach

The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques as often as experiments. MOFs are widely known for their outstanding adsorption properties, so a precise description of the host-guest interactions is essential for high-throughput screening aimed at ranking the most promising candidates. However, highly accurate ab initio calculations cannot be routinely applied to model thousands of structures due to the demanding computational costs. Furthermore, methods based on force field (FF) parametrization suffer from low transferability. To resolve this accuracy-efficiency dilemma, we applied a machine learning (ML) approach: extreme gradient boosting. The trained models reproduced the atom-in-material quantities, including partial charges, polarizabilities, dispersion coefficients, quantum Drude oscillator, and electron cloud parameters, with accuracy similar to the reference data set. The aforementioned FF precursors make it possible to thoroughly describe noncovalent interactions typical for MOF-adsorbate systems: electrostatic, dispersion, polarization, and short-range repulsion. The presented approach can also readily facilitate hybrid atomistic simulation/ML workflows.

Recent advances in simulating gas permeation through MOF membranes

In the last two decades, metal organic frameworks (MOFs) have gained increasing attention in membrane-based gas separations due to their tunable structural properties. Computational methods play a critical role in providing molecular-level information about the membrane properties and identifying the most promising MOF membranes for various gas separations. In this review, we discuss the current state-of-the-art in molecular modeling methods to simulate gas permeation through MOF membranes and review the recent advancements. We finally address current opportunities and challenges of simulating gas permeation through MOF membranes to guide the development of high-performance MOF membranes in the future.

stk: An extendable Python framework for automated molecular and supramolecular structure assembly and discovery

Computational software workflows are emerging as all-in-one solutions to speed up the discovery of new materials. Many computational approaches require the generation of realistic structural models for property prediction and candidate screening. However, molecular and supramolecular materials represent classes of materials with many potential applications for which there is no go-to database of existing structures or general protocol for generating structures. Here, we report a new version of the supramolecular toolkit, stk, an open-source, extendable, and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach works on arbitrary building blocks and topologies and minimizes the input required from the user, making stk user-friendly and applicable to many material classes. This version of stk includes metal-containing structures and rotaxanes as well as general implementation and interface improvements. Additionally, this version includes built-in tools for exploring chemical space with an evolutionary algorithm and tools for database generation and visualization. The latest version of stk is freely available at github.com/lukasturcani/stk.

Capture of toxic gases in MOFs: SO2, H2S, NH3 and NO x

MOFs are promising candidates for the capture of toxic gases since their adsorption properties can be tuned as a function of the topology and chemical composition of the pores. Although the main drawback of MOFs is their vulnerability to these highly corrosive gases which can compromise their chemical stability, remarkable examples have demonstrated high chemical stability to SO2, H2S, NH3 and NO x . Understanding the role of different chemical functionalities, within the pores of MOFs, is the key for accomplishing superior captures of these toxic gases. Thus, the interactions of such functional groups (coordinatively unsaturated metal sites, μ-OH groups, defective sites and halogen groups) with these toxic molecules, not only determines the capture properties of MOFs, but also can provide a guideline for the desigh of new multi-functionalised MOF materials. Thus, this perspective aims to provide valuable information on the significant progress on this environmental-remediation field, which could inspire more investigators to provide more and novel research on such challenging task.

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.

A data-driven perspective on the colours of metal-organic frameworks

Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general. All data is captured in an objective, and standardised, form in an electronic lab notebook and subsequently automatically exported to a repository in open formats, from where it can be interactively explored by other researchers. We envision this to be key for a data-driven approach to chemical research.

Colour is at the core of chemistry and has been fascinating humans since ancient times.