High-throughput computational screening of metal-organic frameworks

Engineered Nanoporous Frameworks for Adsorption Cooling Applications

The energy demand for traditional vapor-compressed technology for space cooling continues to soar year after year due to global warming and the increasing human population's need to improve living and working conditions. Thus, there is a growing demand for eco-friendly technologies that use sustainable or waste energy resources. This review discusses the properties of various refrigerants used for adsorption cooling applications followed by a brief discussion on the thermodynamic cycle. Next, sorbents traditionally used for cooling are reviewed to emphasize the need for advanced capture materials with superior properties to improve refrigerant sorption. The remainder of the review focus on studies using engineered nanoporous frameworks (ENFs) with various refrigerants for adsorption cooling applications. The effects of the various factors that play a role in ENF-refrigerant pair selection, including pore structure/dimension/shape, morphology, open-metal sites, pore chemistry and possible presence of defects, are reviewed. Next, in-depth insights into the sorbent-refrigerant interaction, and pore filling mechanism gained through a combination of characterization techniques and computational modeling are discussed. Finally, we outline the challenges and opportunities related to using ENFs for adsorption cooling applications and provide our views on the future of this technology.

Single-Atom based Metal-Organic Framework Photocatalysts for Solar-Fuel Generation

The growing demand for fossil fuels and subsequent CO2 emissions prompted a search for alternate sources of energy and a reduction in CO2. Photocatalysis driven by solar light has been found as a potential research area to tackle both these problems. In this direction, SAC@MOF (Single-atom loaded MOFs) photocatalysis is an emerging field and a promising technology. The unique properties of single-atom catalysts (SACs), such as high catalytic activity and selectivity, are leveraged in these systems. Photocatalysis, focusing on the utilization of Metal-Organic Frameworks (MOFs) as platforms for creating single-atom catalysts (SACs) characterized by metal single-atoms (SAs) as their active sites, are noted for their unparalleled atomic efficiency, precisely defined active sites, and superior photocatalytic performance. The synergy between MOFs and SAs in photocatalytic systems is meticulously examined, highlighting how they collectively enhance photocatalytic efficiency. This review examines SAC@MOF development and applications in environmental and energy sectors, focusing on synthesis and stabilization methods for SACs on MOFs and also characterization techniques vital for understanding these catalysts. The potential of SAC@MOF in CO2 Photoreduction and Photocatalytic H2 evolution is highlighted, emphasizing its role in green energy technologies and advances in materials science and Photocatalysis.

Frameworked Electrolytes: A Pathway Towards Solid Future of Batteries

All-solid-state batteries (ASSBs) represent a highly promising next-generation energy storage technology owing to their inherently high safety, device reliability, and potential for achieving high energy density in the post-ara of lithium-ion batteries, and therefore extensive searches are ongoing for ideal solid-state electrolytes (SSEs). Though promising, there is still a huge barrier that limits the large-scale applications of ASSBs, where there are a couple of bottleneck technical issues. In this perspective, a novel category of electrolytes known as frameworked electrolytes (FEs) are examined, where the solid frameworks are intentionally designed to contain 3D ionic channels at sub-nano scales, rendering them macroscopically solid. The distinctive structural design of FEs gives rise to not only high ionic conductivity but also desirable interfaces with electrode solids. This is achieved through the presence of sub-nano channels within the framework, which exhibit significantly different ion diffusion behavior due to the confinement effect. This perspective offers a compelling insight into the potential of FEs in the pursuit of ASSBs, where FEs offer an exciting opportunity to overcome the limitations of traditional solid-state electrolytes and propel the development of ASSBs as the holy grail of energy storage technology.

Machine learning guided tuning charge distribution by composition in MOFs for oxygen evolution reaction

Traditional design/optimization of metal-organic frameworks (MOFs) is time-consuming and labor-intensive. In this study, we utilize machine learning (ML) to accelerate the synthesis of MOFs. We have built a library of over 900 MOFs with different metal salts, solvent ratios, reaction durations and temperatures, and utilize zeta potentials as target variables for ML training. A total of four ML models have been used to train the collected dataset and assess their convergence performances, where Random Forest Regression (RFR) and Gradient Boosting Regression (GBR) models show strong correlation and accurate predictions. We then predicted two kinds of MOFs from RFR and GBR models. Remarkably, the experimentally data of the synthesized MOFs closely matched the predicted results, and these MOFs exhibited excellent electrocatalytic performances for oxygen evolution. This study would have general implications in the utilization of machine learning for accelerating the synthesis of MOFs for diverse applications.

Machine Learning-Assisted Discovery of Propane-Selective Metal-Organic Frameworks

Machine learning is gaining momentum in the prediction and discovery of materials for specific applications. Given the abundance of metal-organic frameworks (MOFs), computational screening of the existing MOFs for propane/propylene (C3H8/C3H6) separation could be equally important for developing new MOFs. Herein, we report a machine learning-assisted strategy for screening C3H8-selective MOFs from the CoRE MOF database. Among the four algorithms applied in machine learning, the random forest (RF) algorithm displays the highest degree of accuracy. We experimentally verified the identified top-performing MOF (JNU-90) with its benchmark selectivity and separation performance of directly producing C3H6. Considering its excellent hydrolytic stability, JNU-90 shows great promise in the energy-efficient separation of C3H8/C3H6. This work may accelerate the development of MOFs for challenging separations.

Effect of interlayer slipping on the geometric, thermal and adsorption properties of 2D covalent organic frameworks: a comprehensive review based on computational modelling studies

Two-dimensional covalent organic frameworks (2D-COFs) are a class of crystalline porous organic polymers, consisting of 2D-planar sheets stacked together perpendicularly via noncovalent forces. Since their discovery, 2D-COFs have attracted extensive attention for optoelectronic and adsorption applications. Owing to the layer stacking nature of 2D COFs, various new slipped structures that are energetically favourable can be designed. These interlayer slipped structures are actively responsible for tuning (mostly enhancing) the optoelectronic properties, thermal properties, and mechanical strength of 2D COFs. This review summarizes the effect of interlayer slipping on the energetic stability, electronic behaviour and gas adsorption properties of 2D layered COFs, which is explained through computational modelling simulations. Since computational modelling offers a deep insight into electronic behaviour at the atomic scale, which is potentially impossible through experimental techniques, the introduction and role of computational techniques in such studies have also been described.

Competition between Mononuclear and Binuclear Copper Sites across Different Zeolite Topologies

A key challenge for metal-exchanged zeolites is the determination of metal cation speciation and nuclearity under synthesis and reaction conditions. Copper-exchanged zeolites, which are widely used in automotive emissions control and potential catalysts for partial methane oxidation, have in particular evidenced a wide variety of Cu structures that are observed to change with exposure conditions, zeolite composition, and topology. Here, we develop predictive models for Cu cation speciation and nuclearity in CHA, MOR, BEA, AFX, and FER zeolite topologies using interatomic potentials, quantum chemical calculations, and Monte Carlo simulations to interrogate this vast configurational and compositional space. Model predictions are used to rationalize experimentally observed differences between Cu-zeolites in a wide-body of literature, including nuclearity populations, structural variations, and methanol per Cu yields. Our results show that both topological features and commonly observed Al-siting biases in MOR zeolites increase the population of binuclear Cu sites, explaining the small population of mononuclear Cu sites observed in these materials relative to other zeolites such as CHA and BEA. Finally, we used a machine learning classification model to determine the preference to form mononuclear or binuclear Cu sites at different Al configurations in 200 zeolites in the international zeolite database. Model results reveal several zeolite topologies at extreme ends of the mononuclear vs binuclear spectrum, highlighting synthetic options for realization of zeolites with strong Cu nuclearity preferences.

The future of metal-organic frameworks and covalent organic frameworks: rational synthesis and customized applications

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are designable and tunable functional crystalline porous materials that have been explored for applications such as catalysis, chemical sensing, water harvesting, gas storage, and separation. On the basis of reticular chemistry, the rational design and synthesis of MOFs and COFs allows us to have unprecedented control over their structural features and functionalities. Given the vast number of possible MOF and COF structures and the flexibility of modifying them, it remains challenging to navigate the infinite chemical space solely through a trial-and-error process. This Opinion Article provides a brief perspective of the current state and future prospects of MOFs and COFs. We envision that emerging technologies based on machine learning and robotics, such as high-throughput computational screening and fully automatic synthesis, can potentially address some challenges facing this field, accelerating the discovery of porous framework materials and the development of rational synthetic strategies for customized applications.

Leveraging Machine Learning for Metal-Organic Frameworks: A Perspective

Metal-organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or brute-force simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure-property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field.

Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning

As the world grapples with the challenges of energy transition and industrial decarbonization, the development of carbon capture technologies presents a promising solution. The Scalable Modeling, Artificial Intelligence (AI), and Rapid Theoretical calculations, referred as SMART here, is an interdisciplinary approach that combines high-throughput calculation and data-driven modeling with expertise from chemical, materials, environmental, computer and data science and engineering, leading to the development of advanced capabilities in simulating and optimizing carbon capture processes. This perspective discusses the state-of-the-art material discovery research enabled by high-throughput calculation and data-driven modeling. Further, we propose a framework for material discovery, and illustrate the synergies among deep learning models, pretrained models, and comprehensive data sets, emerging as a robust framework for data-driven design and development in carbon capture. In essence, the adoption of the SMART approach promises a revolutionary impact on efforts in energy transition and industrial decarbonization.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Heterogenization of molecular catalysts within porous solids: the case of Ni-catalyzed ethylene oligomerization from zeolites to metal-organic frameworks

The last decade has seen a tremendous expansion of the field of heterogenized molecular catalysis, especially with the growing interest in metal-organic frameworks and related porous hybrid solids. With successful achievements in the transfer from molecular homogeneous catalysis to heterogenized processes come the necessary discussions on methodologies used and a critical assessment on the advantages of heterogenizing molecular catalysis. Here we use the example of nickel-catalyzed ethylene oligomerization, a reaction of both fundamental and applied interest, to review heterogenization methodologies of well-defined molecular catalysts within porous solids while addressing the biases in the comparison between original molecular systems and heterogenized counterparts.

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance.

MOF-Based Chemiresistive Gas Sensors: Toward New Functionalities

The sensing performances of gas sensors must be improved and diversified to enhance quality of life by ensuring health, safety, and convenience. Metal-organic frameworks (MOFs), which exhibit an extremely high surface area, abundant porosity, and unique surface chemistry, provide a promising framework for facilitating gas-sensor innovations. Enhanced understanding of conduction mechanisms of MOFs has facilitated their use as gas-sensing materials, and various types of MOFs have been developed by examining the compositional and morphological dependences and implementing catalyst incorporation and light activation. Owing to their inherent separation and absorption properties and catalytic activity, MOFs are applied as molecular sieves, absorptive filtering layers, and heterogeneous catalysts. In addition, oxide- or carbon-based sensing materials with complex structures or catalytic composites can be derived by the appropriate post-treatment of MOFs. This review discusses the effective techniques to design optimal MOFs, in terms of computational screening and synthesis methods. Moreover, the mechanisms through which the distinctive functionalities of MOFs as sensing materials, heterostructures, and derivatives can be incorporated in gas-sensor applications are presented.

DFT-Quality Adsorption Simulations in Metal-Organic Frameworks Enabled by Machine Learning Potentials

Nanoporous materials such as metal-organic frameworks (MOFs) have been extensively studied for their potential for adsorption and separation applications. In this respect, grand canonical Monte Carlo (GCMC) simulations have become a well-established tool for computational screenings of the adsorption properties of large sets of MOFs. However, their reliance on empirical force field potentials has limited the accuracy with which this tool can be applied to MOFs with challenging chemical environments such as open-metal sites. On the other hand, density-functional theory (DFT) is too computationally demanding to be routinely employed in GCMC simulations due to the excessive number of required function evaluations. Therefore, we propose in this paper a protocol for training machine learning potentials (MLPs) on a limited set of DFT intermolecular interaction energies (and forces) of CO2 in ZIF-8 and the open-metal site containing Mg-MOF-74, and use the MLPs to derive adsorption isotherms from first principles. We make use of the equivariant NequIP model which has demonstrated excellent data efficiency, and as such an error on the interaction energies below 0.2 kJ mol-1 per adsorbate in ZIF-8 was attained. Its use in GCMC simulations results in highly accurate adsorption isotherms and heats of adsorption. For Mg-MOF-74, a large dependence of the obtained results on the used dispersion correction was observed, where PBE-MBD performs the best. Lastly, to test the transferability of the MLP trained on ZIF-8, it was applied to ZIF-3, ZIF-4, and ZIF-6, which resulted in large deviations in the predicted adsorption isotherms and heats of adsorption. Only when explicitly training on data for all ZIFs, accurate adsorption properties were obtained. As the proposed methodology is widely applicable to guest adsorption in nanoporous materials, it opens up the possibility for training general-purpose MLPs to perform highly accurate investigations of guest adsorption.

High-Throughput Screening of the CoRE-MOF-2019 Database for CO2 Capture from Wet Flue Gas: A Multi-Scale Modeling Strategy

Stabilizing the escalating CO₂ levels in the atmosphere is a grand challenge in view of the increasing global demand for energy, the majority of which currently comes from the burning of fossil fuels. Capturing CO₂ from point source emissions using solid adsorbents may play a part in meeting this challenge, and metal-organic frameworks (MOFs) are considered to be a promising class of materials for this purpose. It is important to consider the co-adsorption of water when designing materials for CO₂ capture from post-combustion flue gases. Computational high-throughput screening (HTS) is a powerful tool to identify top-performing candidates for a particular application from a large material database. Using a multi-scale modeling strategy that includes a machine learning model, density functional theory (DFT) calculations, force field (FF) optimization, and grand canonical Monte Carlo (GCMC) simulations, we carried out a systematic computational HTS of the all-solvent-removed version of the computation-ready experimental metal-organic framework (CoRE-MOF-2019) database for selective adsorption of CO₂ from a wet flue gas mixture. After initial screening based on the pore diameters, a total of 3703 unique MOFs from the database were considered for screening based on the FF interaction energies of CO₂, N₂, and H₂O molecules with the MOFs. MOFs showing stronger interactions with CO₂ compared to that with H₂O and N₂ were considered for the next level of screening based on the interaction energies calculated from DFT. CO₂-selective MOFs from DFT screening were further screened using two-component (CO₂ and N₂) and finally three-component (CO₂, N₂, and H₂O) GCMC simulations to predict the CO₂ capacity and CO₂/N₂ selectivity. Our screening study identified MOFs that show selective CO₂ adsorption under wet flue gas conditions with significant CO₂ uptake capacity and CO₂/N₂ selectivity in the presence of water vapor. We also analyzed the nature of pore confinements responsible for the observed CO₂ selectivity.

CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture applications. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO₂ and N₂ adsorption isotherms on 690 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), six partial charge schemes (no charges, Qeq, EQeq, MPNN, PACMOF, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms.

Early-Stage Formation of the SIFSIX-3-Zn Metal-Organic Framework: An Automated Computational Study

Metal-organic frameworks (MOFs) have attracted significant attention over the past 2 decades due to their wide applicability as functional materials. However, targeted synthesis of novel MOFs remains problematic as their formation mechanisms are poorly understood, which forces us to rely on serendipity in the synthesis of novel MOFs. Here, we demonstrate a workflow employing the artificial force induced reaction (AFIR) method to investigate the self-assembly process of the node of the SIFSIX-3-Zn MOF, [Zn(pyz)₄(SiF₆)₂]^2- (pyz = pyrazine), in an automated manner. The workflow encompassing AFIR calculations, generation of extensive reaction path networks, propagation simulations of intermediates, and further refinements of identified formation pathways showed that the nodal structure can form through multiple competing pathways involving interconvertible intermediates. This finding provides a plausible rationale for the stochastic multistage processes believed to be key in MOF formation. Furthermore, this work represents the first application of an automated reaction mechanism discovery method to a MOF system using a general workflow that is applicable to study the formation of other MOF motifs as well.

Ligand-Functional Groups Induced Tuning MOFs' 2D into 1D Pore Channels for Pipeline Natural Gas Purification

The solvothermal reactions of CoCl₂  ⋅ 6H₂ O, 3,5-pyridinedicarboxylic acid (H₂ L) and isonicotinic acid (HL₁ )/3-amino isonicotinic acid (HL₂ )/3-chloro isonicotinic acid (HL₃ ) successfully led to three tfz-d topological pillar-layer [Co₄ (μ-F)₂ (COO)₆ (NC₅ H₄ )₄ ] cluster-based MOFs, namely, [Co₄ (μ-F)₂ (L)₂ (L₁ )₂  ⋅ 2DMA] ⋅ DMA ⋅ 2H₂ O (SNNU-Bai76, SNNU-Bai=Shaanxi Normal University Bai's group), [Co₄ (μ-F)₂ (L)₂ (L₂ )₂  ⋅ 2H₂ O] ⋅ 2DMA ⋅ 2H₂ O (SNNU-Bai77) and [Co₄ (μ-F)₂ (L)₂ (L₃ )₂  ⋅ 2H₂ O] ⋅ 2DMF ⋅ 2H₂ O (SNNU-Bai78). With the 2D pore channels in SNNU-Bai76 and SNNU-Bai77 being tuned to the 1D pore channel in SNNU-Bai78, C₃ H₈ and C₂ H₆ adsorption uptakes are apparently improved and the IAST selectivities of C₃ H₈ /CH₄ and C₂ H₆ /CH₄ almost remain, which indicate that SNNU-Bai78 may be one potential separation material for the pipeline natural gas purification. These were further confirmed by the breakthrough experiments for the simulated pipeline natural gas (C₃ H₈ /C₂ H₆ /CH₄  : 5/10/85 gas mixture) of three isostructural MOFs. Furthermore, GCMC simulations revealed that due to one of the pore channels blocked by Cl atoms in a couple of 3-chloro isonicotinic acid with the changed conformation as the pillar, the pore wall of the formed 1D pore channel in SNNU-Bai78 may interact with the adsorbed C₃ H₈ or C₂ H₆ molecule more strongly, for which more atoms of framework at the new adsorption site will interact with the adsorbed gas molecule by more intermolecular interactions. This was also evidenced by the increased binding energies, being consistent with the tuning of adsorption enthalpies for C₃ H₈ and C₂ H₆ gas molecules, and the reduced C₃ H₈ and C₂ H₆ gas diffusion coefficients in SNNU-Bai78. Very interestingly, this work is the first example of finely tuning the pore connectivity of MOFs toward strengthened host-guest interactions for the gas adsorption and separation.

Electrostatic potential-derived charge: a universal OER performance descriptor for MOFs

Metal-organic frameworks (MOFs) provide opportunities for the design of high-efficiency catalysts attributed to their high compositional and structural tunability. Meanwhile, the huge number of MOFs poses a great challenge to experimental-intensive development of high-performance functional applications. By taking the computationally feasible and structurally representative trigonal prismatic secondary building units (SBUs) of MOFs as the entry point, we introduce a descriptor-based approach for designing high-performance MOFs for the oxygen evolution reaction (OER). The electrostatic potential-derived charge (ESPC) is identified as a robust and universal OER performance descriptor of MOFs, showing a distinct linear relationship with the onset potentials of OER elemental steps. Importantly, we establish an ESPC-based physical pattern of active site-intermediate binding strength, which interprets the rationality of ESPC as an OER performance descriptor. We further reveal that the SBUs with Ni/Cu as active site atoms while Mn/Fe/Co/Ni as spectator atoms have excellent OER activity through the variation pattern of ESPC along with metal composition. The universal correlation between ESPC and OER activity provides a rational rule for designing high-performance MOF-based OER electrocatalysts and can be easily extended to design functional MOFs for a rich variety of catalytic applications.

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.

The HEALED SBU Library of Chemically Realistic Building Blocks for Construction of Hypothetical Metal-Organic Frameworks

Advancements in hypothetical metal-organic framework (hMOF) databases and construction tools have resulted in a rapidly expanding chemical design space for nanoporous materials. The bulk of these hypothetical structures are constructed using structural building units (SBUs) derived from experimental MOF structures, often collected from the CoRE-MOF database. Recent investigations into the state of these deposited experimental structures' chemical accuracy identified an array of common structural errors, including omitted protons, missing counterions, and disordered structures. These structural errors propagate into the SBUs mined from experimental MOFs, culminating in inaccurate hMOF structures possessing net charges or missing atoms which were not accounted for previously. This work demonstrates how manual investigation was applied to diagnose structural errors in SBUs obtained from several popular hMOF construction tools and databases. An analysis of the prevailing errors discovered during the examination process is provided along with representative cases to aid with error detection in future studies involving SBU extraction and hMOF construction. A novel repair protocol was established and employed to generate a library of SBUs that are hand-examined and labeled with enhanced detail (HEALED). This repaired library of SBUs contains 952 inorganic SBUs and 568 organic SBUs ideally suited for the generation of hypothetical frameworks that are chemically accurate and properly charge labeled. Additionally, case studies following the effects of SBU errors on electrostatic potential-fitted charges and GCMC-simulated gas adsorption predictions are presented to highlight the significance of using chemically accurate hMOF structures exclusively in all screening efforts going forward.

Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks

Energy-related descriptors in machine learning are a promising strategy to predict adsorption properties of metal–organic frameworks (MOFs) in the low-pressure regime. Interactions between hosts and guests in these systems are typically expressed as a sum of dispersion and electrostatic potentials. The energy landscape of dispersion potentials plays a crucial role in defining Henry’s constants for simple probe molecules in MOFs. To incorporate more information about this energy landscape, we introduce the Gaussian-approximated Lennard-Jones (GALJ) potential, which fits pairwise Lennard-Jones potentials with multiple Gaussians by varying their heights and widths. The GALJ approach is capable of replicating information that can be obtained from the original LJ potentials and enables efficient development of Gaussian integral (GI) descriptors that account for spatial correlations in the dispersion energy environment. GI descriptors would be computationally inconvenient to compute using the usual direct evaluation of the dispersion potential energy surface. We show that these new GI descriptors lead to improvement in ML predictions of Henry’s constants for a diverse set of adsorbates in MOFs compared to previous approaches to this task.

Theory-guided design of hydrogen-bonded cobaltoporphyrin frameworks for highly selective electrochemical H2O2 production in acid

The pursuit of selective two-electron oxygen reduction reaction to H2O2 in acids is demanding and largely hampered by the lack of efficient non-precious-metal-based electrocatalysts. Metal macrocycles hold promise, but have been relatively underexplored. Efforts are called for to promote their inherent catalytic activities and/or increase the surface exposure of active sites. In this contribution, we perform the high-throughput computational screening of thirty-two different metalloporphyrins by comparing their adsorption free energies towards key reaction intermediates. Cobalt porphyrin is revealed to be the optimal candidate with a theoretical overpotential as small as 40 mV. Guided by the computational predictions, we prepare hydrogen-bonded cobaltoporphyrin frameworks in order to promote the solution accessibility of catalytically active sites for H2O2 production in acids. The product features an onset potential at ~0.68 V, H2O2 selectivity of >90%, turnover frequency of 10.9 s-1 at 0.55 V and stability of ~30 h, the combination of which clearly renders it stand out from existing competitors for this challenging reaction.

Unlocking the computational design of metal-organic cages

Metal-organic cages are macrocyclic structures that can possess an intrinsic void that can hold molecules for encapsulation, adsorption, sensing, and catalysis applications. As metal-organic cages may be comprised from nearly any combination of organic and metal-containing components, cages can form with diverse shapes and sizes, allowing for tuning toward targeted properties. Therefore, their near-infinite design space is almost impossible to explore through experimentation alone and computational design can play a crucial role in exploring new systems. Although high-throughput computational design and screening workflows have long been known as powerful tools in drug and materials discovery, their application in exploring metal-organic cages is more recent. We show examples of structure prediction and host-guest/catalytic property evaluation of metal-organic cages. These examples are facilitated by advances in methods that handle metal-containing systems with improved accuracy and are the beginning of the development of automated cage design workflows. We finally outline a scope for how high-throughput computational methods can assist and drive experimental decisions as the field pushes toward functional and complex metal-organic cages. In particular, we highlight the importance of considering realistic, flexible systems.

PoreMatMod.jl: Julia Package for in Silico Postsynthetic Modification of Crystal Structure Models

PoreMatMod.jl is a free, open-source, user-friendly, and documented Julia package for modifying crystal structure models of porous materials such as metal-organic frameworks (MOFs). PoreMatMod.jl functions as a find-and-replace algorithm on crystal structures by leveraging (i) Ullmann's algorithm to search for subgraphs of the crystal structure graph that are isomorphic to the graph of a query fragment and (ii) the orthogonal Procrustes algorithm to align a replacement fragment with a targeted substructure of the crystal structure for installation. The prominent application of PoreMatMod.jl is to generate libraries of hypothetical structures for virtual screenings. For example, one can install functional groups on the linkers of a parent MOF, mimicking postsynthetic modification. Other applications of PoreMatMod.jl to modify crystal structure models include introducing defects with precision and correcting artifacts of X-ray structure determination (adding missing hydrogen atoms, resolving disorder, and removing guest molecules). The find-and-replace operations implemented by PoreMatMod.jl can be applied broadly to diverse atomistic systems for various in silico structural modification tasks.

Multi-functional metal-organic frameworks for detection and removal of water pollutions

Water pollutions have caused serious threats to the aquatic environment and human health, it is of great significance to monitor and control their contents in water. Compared with the traditional...

From computational high-throughput screenings to the lab: taking metal–organic frameworks out of the computer

Computational high-throughput screenings (HTS) have become a standard method of sieving the vast amount of metal–organic frameworks (MOFs) data. But not many HTS studies have been able to bring MOFs to the lab.

Building Unit Extractor for Metal-Organic Frameworks

Metal-organic frameworks (MOFs) have relevance in extensive applications such as gas adsorption, separation, and energy storage. The tunability demonstrated by MOFs has encouraged research on MOF database generation via distinct methodologies. One of the crucial stages of these procedures is pre-processing, which often includes extraction of the building units (BUs). The process of BU extraction is intricate, and it is further amplified with the presence of solvent molecules/ions in the structure. This work presents MOF BU developer (mBUD), a platform to deconstruct the BUs, such as metal nodes, organic linkers, and functional groups of the MOF structure. The deconstruction algorithm has been assessed on the MOF structures of the CoRE MOF 2019 database. A total of 2,580 BUs have been extracted and provided as a database. This platform has been utilized to create a ready-to-use database of unique BUs deconstructed from the CoRE MOF database. We have also provided the web version of mBUD that can be easily used to extract BUs. These BUs can be employed to develop hypothetical MOF structures. It is envisaged that the BU database built with the deconstruction platform will aid the design of novel application-specific MOFs.

Concluding remarks: current and next generation MOFs

This paper describes the content of my "Concluding remarks" talk at the Faraday Discussion meeting on "MOFs for energy and the environment" (online, 23-25 June 2021). The panel consisted of sessions on the design of MOFs and MOF hybrids (synthetic chemistry), their applications (e.g., capture, storage, separation, electrical devices, photocatalysis), advanced characterization (e.g., transmission electron microscopy, solid-state nuclear magnetic resonance), theory and modeling, and commercialization. MOF chemistry is undergoing a significant evolution from simply network chemistry to the chemistry of synergistic integration with heterogeneous materials involving other disciplines (we call this the fourth generation type). As reflected in the papers of the invited speakers and discussions with the participants, the present and future of this field will be described in detail.

A high-throughput screening of metal-organic framework based membranes for biogas upgrading

Applications of biomethane as a source of renewable energy and transport fuel rely heavily on successful implementation of purification methods capable of removing undesirable impurities from biogas and increasing its calorific content. Metal-organic frameworks (MOFs) are competitive candidates for biogas upgrading due to a versatile range of attractive physical and chemical properties which can be utilised in membrane materials. In this work, we present a high-throughput computational screening methodology for efficient identification of MOF structures with promising gas separation performance. The proposed screening strategy is based on initial structural analysis and predictions of the single-component permeation of CO₂, CH₄ and H₂S from adsorption and diffusion calculations at infinite dilution. The identified top performing candidates are subject to further analysis of their gas separation performance at the operating conditions of 10 bar and 298 K, using grand canonical Monte Carlo and equilibrium molecular dynamics simulations on equimolar CO₂/CH₄ and H₂S/CH₄ mixtures. The Henry constant for the adsorption of H₂O was also calculated to determine the hydrophobicity of MOF structures, as the presence of H₂O often leads to membrane instability and performance limitations. For the considered gas mixtures, the top MOF candidates exhibit superior separation capabilities over polymer-, zeolite-, and mixed matrix-based membranes as indicated by the predicted values of selectivity and permeability. The proposed screening protocol offers a powerful tool for the rational design of novel MOFs for biogas upgrading.

Thermodynamic exploration of xenon/krypton separation based on a high-throughput screening

Nanoporous framework materials are a promising class of materials for energy-efficient technology of xenon/krypton separation by physisorption. Many studies on Xe/Kr separation by adsorption have focused on the determination of structure/property relationships, the description of theoretical limits of performance, and the identification of top-performing materials. Here, we provide a study based on a high-throughput screening of the adsorption of Xe, Kr, and Xe/Kr mixtures in 12 020 experimental MOF materials, to provide a better comprehension of the thermodynamics behind Xe/Kr separation in nanoporous materials and the microscopic origins of Xe/Kr selectivity at both low and ambient pressure.

Gas adsorption and separation in metal-organic frameworks by PC-SAFT based density functional theory

In this work, we examine the theoretical performance of perturbed-chain statistical associating fluid theory based density functional theory (DFT) in predicting gas adsorption and separation in metal-organic frameworks by using simulation and experimental data as the benchmark. Adsorption isotherms of methane and ethane in pure gas and mixtures and selectivities for ethane/methane mixtures are calculated. The predicted isotherms by DFT are in excellent agreement with simulation and experimental data for pure methane and ethane, whereas for the mixture, DFT is in semi-quantitative accordance with simulation results. For fast and high-throughput screening of material purpose, three algorithms including Picard iteration with line search, Anderson mixing, and Picard-Anderson-hybrid algorithm are proposed to calculate the three dimensional density distribution of confined gases. The advantages and limitations of the three algorithms under various conditions are discussed.