High-Throughput Screening of MOF Adsorbents and Membranes for H2 Purification and CO2 Capture

Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation

The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity. In this work, we introduce MOFFUSION, a latent diffusion model that addresses the aforementioned challenges. Signed distance functions (SDFs) are employed for the input representation of MOFs, marking their first usage in representing porous materials for generative models. Using the suitability of SDFs in describing complicated pore structures, MOFFUSION exhibits exceptional generation performance, and demonstrates its versatile capability of conditional generation with handling diverse modalities of data, including numeric, categorical, text data, and their combinations.

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.

Carbon Dioxide Capture on Oxygen- and Nitrogen-Containing Carbon Quantum Dots

To address global climate change challenges, an effective strategy involves capturing CO2 directly at its source using a sustainable, low-cost adsorbent. Carbon quantum dots (CQDs), derived from lignin, are employed to modify the internal surface of an activated carbon adsorbent, enabling selective adsorption based on electrostatic interactions. By manipulating charge distribution on CQDs through either doping (nitrogen) or functionalization (amine, carboxyl, or hydroxyl groups), the study confirms, through classical molecular dynamics simulations, the potential to adjust binding strength, adsorption capacity, and selectivity for CO2 over N2 and O2. For simulations with a single component gas, maximum selectivities of 3.6 and 6.7 are shown for CO2/N2 and CO2/O2, respectively, at 300 K. Simulations containing a wet flue gas indicate that the presence of water increases the CO2/N2 and CO2/O2 selectivities. The highest CO2/H2O selectivity obtained from a CQD/graphite system is 4.3. A comparison of graphite and lignin-based carbon composite (LBCC) substrates demonstrated that LBCC has enhanced adsorptive capacity. The roughness of the LBCC substrate prevents the diffusion of the CQD on the surface. This computational study takes another step toward identifying optimal CQD atomic architecture, dimensions, doping, and functionalization for a large-scale CQD/AC adsorbent solution for CO2 capture.

Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications

Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high-throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO2 capture, CH4 storage, H2 storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs' performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.

Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal-Organic Frameworks for Hydrogen Storage

Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10-4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

Machine learning insights into predicting biogas separation in metal-organic frameworks

Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation properties of metal-organic frameworks (MOFs). Training data are obtained using grand canonical Monte Carlo simulations of experimental MOFs which have been carefully curated to ensure data quality and structural viability. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R2 values consistently above 0.9 for the validation set. We make prospective predictions on an independent external set of hypothetical MOFs, and examine these predictions in comparison to the results of grand canonical Monte Carlo calculations. The best-performing trained models correctly filter out over 90% of low-performing unseen MOFs, illustrating their applicability to other MOF datasets.

Efficient CO2 Capture and Separation in MOFs: Effect from Isoreticular Double Interpenetration

Severe CO2 emissions has posed an increasingly alarming threat, motivating the development of efficient CO2 capture materials, one of the key parts of carbon capture, utilization, and storage (CCUS). In this study, a series of metal-organic frameworks (MOFs) named Sc-X (X = S, M, L) were constructed inspired by recorded MOFs, Zn-BPZ-SA and MFU-4l-Li. The corresponding isoreticular double-interpenetrating MOFs (Sc-X-IDI) were subsequently constructed via the introduction of isoreticular double interpenetration. Grand canonical Monte Carlo (GCMC) simulations were adopted at 298 K and 0.1-1.0 bar to comprehensively evaluate the CO2 capture and separation performances in Sc-X and Sc-X-IDI, with gas distribution, isothermal adsorption heat (Qst), and van der Waals (vdW)/Coulomb interactions. It is showed that isoreticular double interpenetration significantly improved the interactions between adsorbed gases and frameworks by precisely modulating pore sizes, particularly observed in Sc-M and Sc-M-IDI. Specifically, the Qst and Coulomb interactions exhibited a substantial increase, rising from 28.38 and 22.19 kJ mol-1 in Sc-M to 43.52 and 38.04 kJ mol-1 in Sc-M-IDI, respectively, at 298 K and 1.0 bar. Besides, the selectivity of CO2 over CH4/N2 was enhanced from 55.36/107.28 in Sc-M to 3308.61/7021.48 in Sc-M-IDI. However, the CO2 capture capacity is significantly influenced by the pore size. Sc-M, with a favorable pore size, exhibits the highest capture capacity of 15.86 mmol g-1 at 298 K and 1.0 bar. This study elucidated the impact of isoreticular double interpenetration on the CO2 capture performance in MOFs.

Accelerated Discovery of Metal-Organic Frameworks for CO2 Capture by Artificial Intelligence

The existence of a very large number of porous materials is a great opportunity to develop innovative technologies for carbon dioxide (CO2) capture to address the climate change problem. On the other hand, identifying the most promising adsorbent and membrane candidates using iterative experimental testing and brute-force computer simulations is very challenging due to the enormous number and variety of porous materials. Artificial intelligence (AI) has recently been integrated into molecular modeling of porous materials, specifically metal-organic frameworks (MOFs), to accelerate the design and discovery of high-performing adsorbents and membranes for CO2 adsorption and separation. In this perspective, we highlight the pioneering works in which AI, molecular simulations, and experiments have been combined to produce exceptional MOFs and MOF-based composites that outperform traditional porous materials in CO2 capture. We outline the future directions by discussing the current opportunities and challenges in the field of harnessing experiments, theory, and AI for accelerated discovery of porous materials for CO2 capture.

Breaking The Permeance-Selectivity Tradeoff for Post-Combustion Carbon Capture: A Bio-Inspired Strategy to Form Ultrathin Hollow Fiber Membranes

Thin film composite (TFC) hollow fiber membranes with ultrathin selective layer are desirable to maximize the gas permeance for practical applications. Herein, a bio-inspired strategy is proposed to fabricate sub-100-nm membranes via a tree-mimicking polymer network with amphipathic components featuring multifunctionalities. The hydrophobic polydimethylsiloxane (PDMS) brushes act as the roots that can strongly cling to the gutter layer, the PDMS crosslinkers function as the xylems to enable fast gas transport, and the hydrophilic ethylene-oxide moieties (brushes and mobile molecules) resemble tree leaves that selectively attract CO2 molecules. As a result, a ≈27 nm-thick selective layer can be attached to the hollow fiber-supported PDMS gutter layer through a simple dip-coating method without any modification. Furthermore, a CO2 permeance of ≈2700 GPU and a CO2 /N2 selectivity of ≈21 that is beyond the permeance-selectivity upper bound for hollow fiber membranes is achieved. This bio-inspired concept can potentially open the possibility of scalable hollow fiber membranes production for commercial applications in post-combustion carbon capture and beyond.

Prediction of Xe/Kr Separation in Metal-Organic Frameworks by a Precursor-Based Neural Network Synergistic with a Polarizable Adsorbate Model

Adsorption and separation of Xe/Kr are significant for making high-density nuclear energy environmentally friendly and for meeting the requirements of the gas industry. Enhancing the accuracy of the adsorbate model for describing the adsorption behaviors of Xe and Kr in MOFs and the efficiency of the model for predicting the separation potential (SP) value of Xe/Kr separation in MOFs helps in searching for promising MOFs for Xe/Kr adsorption and separation within a short time and at a low cost. In this work, polarizable and transferable models for mimic Xe and Kr adsorption behaviors in MOFs were constructed. Using these models, SP values of 38 MOFs at various temperatures and pressures were calculated. An optimal neural network model called BPNN-SP was designed to predict SP value based on physical parameters of metal center (electronegativity and radius) and organic linker (three-dimensional size and polarizability) combined with temperature and pressure. The regression coefficient value of the BPNN-SP model for each data set is higher than 0.995. MAE, MBE, and RMSE of BPNN-SP are only 0.331, -0.002, and 0.505 mmol/g, respectively. Finally, BPNN-SP was validated by experiment data from six MOFs. The transferable adsorbate model combined with the BPNN-SP model would highly improve the efficiency for designing MOFs with high performance for Xe/Kr adsorption and separation.

Combination of High-Throughput Screening and Assembly to Discover Efficient Metal-Organic Frameworks on Kr/Xe Adsorption Separation

Recycling Kr and Xe from used nuclear fuel (UNF) is conducive to regenerating economy and protecting the environment, and it is urgent to screen or design high-performance cutting-edge metal-organic framework (MOF) materials for Kr/Xe adsorption separation. After grand canonical Monte Carlo (GCMC) simulations of Kr/Xe adsorption separation on 11,000 frameworks in CoRE MOFs (2019), the important structure-adsorption property relationship (SAPR) was induced; that is, the porosity (φ) at 0.30-0.40, LCD/PLD at 1.00-1.49, density (ρ) range between 1.20 and 2.30 g/cm3, and PLD at 2.40-3.38 Å can be utilized to screen for high-performance G-MOFs and hMOFs. In addition, the key "genes" (metal nodes and linkers) of MOFs determining the Kr/Xe adsorption separation were data-mined by a machine learning technique, which were assembled into novel MOFs. After comprehensive consideration of thermal stability and the adsorbent performance score (APS), eight promising MOFs on Kr/Xe separation with the APS more than 1290.89 were screened out and assembled, which are better than most of the reported frameworks. Note that the adsorption isotherms of these MOFs on Kr and Xe belong to type I curve with the thermodynamic equilibrium mechanism on Kr/Xe based on the confinement effect. Furthermore, according to the electronic structure calculations of the independent gradient model based on Hirshfeld partition (IGMH) and energy decomposition analysis, it is found that the interactions between guests and frameworks are vdW forces with dominant induction energy (Eind). In addition, the electrostatic potential gradients of frameworks are generally linearly negative correlated with Kr uptakes. Therefore, both the geometrical and electronic structures dominate the adsorption separation performance on Kr/Xe. Interestingly, these eight MOFs are also suitable for the separation of CH4/H2 with considerable selectivities and CH4 uptakes of up to 2566.67 and 3.04 mmol/g, respectively. Herein, the accurately constructed SAPR and material genomics strategy should be helpful for the experimental discovery of novel MOFs on Kr/Xe separation experimentally.

Adsorptive separation of CH4, H2, CO2, and N2 using fullerene pillared graphene nanocomposites: Insights from molecular simulations

CONTEXT

The adsorptive separation performances of fullerene pillared graphene nanocomposites (FPGNs) with tunable micro and meso porous morphology are investigated for the binary mixtures of CH4, H2, CO2 and N2 by using grand canonical Monte Carlo (GCMC) simulations. Different fullerene types are considered in designs as pillar to investigate the effects of porosity on the gas separation performances of FPGNs, and the GCMC simulations are performed for an equimolar binary mixture of CO2/H2, CO2/CH4, CO2/N2 and CH4/H2 inspired by industrial gas mixtures. It is found that CO2/N2, CO2/H2 and CH4/H2 selectivity of FPGNs are about 72, 410 and 145 at 298 K and 1 bar, which are higher than those for several adsorbent materials reported.

METHODS

Five different FPGN models which contain covalently bonded periodical fullerene and graphene units were constructed using C60, C180, C320, C540 and C720 fullerenes, followed by geometry optimization using Open Babel. All GCMC simulations of adsorption were performed in the RASPA. The adsorption isotherms of FPGNs for pure gases are comparatively examined, and their performances are discussed based on the pore structure and isosteric heat of adsorption. Then, the separation factors of FPGNs for equimolar binary mixtures of these gases are elucidated from the difference in the heat of adsorption and the adsorption selectivity.

Disclosing gate-opening/closing events inside a flexible metal-organic framework loaded with CO2 by reactive and essential dynamics

We have combined reactive molecular dynamics simulations with principal component analysis to provide a clearer view of the interactions and motion of the CO2 molecules inside a metal-organic framework and the movements of the MOF components that regulate storage, adsorption, and diffusion of the guest species. The tens-of-nanometer size of the simulated model, the capability of the reactive force field tuned to reproduce the inorganic-organic material confidently, and the unconventional use of essential dynamics have effectively disclosed the gate-opening/closing phenomenon, possible coordinations of CO2 at the metal centers, all the diffusion steps inside the MOF channels, the primary motions of the linkers, and the effects of their concerted rearrangements on local CO2 relocations.

DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

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.

Challenges in Developing MOF-Based Membranes for Gas Separation

Metal-organic frameworks (MOFs) are promising candidates for membrane gas separation. MOF-based membranes include pure MOF membranes and MOF-based mixed matrix membranes (MMMs). This Perspective discusses the challenges for the next stage of the development of MOF-based membranes based on research conducted in the past decade. We focused on three major issues associated with pure MOF membranes. First, some MOF compounds have been overstudied, despite the availability of numerous MOFs. Second, gas adsorption and diffusion in MOFs are often independently investigated. The correlation between adsorption and diffusion has seldom been discussed. Third, we identify the importance of characterizing the gas distribution in MOFs to understand the structure-property relationships for gas adsorption and diffusion in MOF membranes. For MOF-based MMMs, engineering the MOF-polymer interface is essential for achieving the desired separation performance. Various approaches to modify the MOF surface or polymer molecular structure have been proposed to improve the MOF-polymer interface. Herein, we present defect engineering as a facile and efficient approach for engineering the MOF-polymer interfacial morphology and its extended application for various gas separations.

Exploring the Potential of a Highly Scalable Metal-Organic Framework CALF-20 for Selective Gas Adsorption at Low Pressure

In this study, the ability of the highly scalable metal-organic framework (MOF) CALF-20 to adsorb polar and non-polar gases at low pressure was investigated using grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The results from the simulated adsorption isotherms revealed that the highest loading was achieved for SO2 and Cl2, while the lowest loading was found for F2 molecules. The analysis of interaction energies indicated that SO2 molecules were able to form the strongest adsorbent-adsorbate interactions and had a tight molecular packing due to their polarity and angular structure. Additionally, Cl2 gas was found to be highly adsorbed due to its large van der Waals surface and strong chemical affinity in CALF-20 pores. MD simulations showed that SO2 and Cl2 had the lowest mobility inside CALF-20 pores. The values of the Henry coefficient and isosteric heat of adsorption confirmed that CALF-20 could selectively adsorb SO2 and Cl2. Based on the results, it was concluded that CALF-20 is a suitable adsorbent for SO2 and Cl2 but not for F2. This research emphasizes the importance of molecular size, geometry, and polarity in determining the suitability of a porous material as an adsorbent for specific adsorbates.

Curated Collection of More than 20,000 Experimentally Reported One-Dimensional Metal-Organic Frameworks

A collection of more than 20,000 experimentally derived crystal structures for metal-organic frameworks (MOFs) that do not have two- or three-dimensional covalently bonded networks has been developed from the materials available at the Cambridge Crystallographic Data Centre. Of these 20,000 1D MOFs, more than 12,000 structures have been verified to be solvent-free and in exact agreement with the stoichiometry of the synthesized materials. More than 10% of the complete data set comprise materials including two or more distinct metals. The band gaps of more than 12,000 1D MOFs have been computed at the density functional theory-generalized gradient approximation level, finding more than 2000 materials that have a zero band gap. Molecular simulations of CH₄ adsorption in a small number of 1D MOFs indicated that adsorbate-induced deformation plays a significant role in determining adsorption isotherms in these materials. As a result, methods that have been used previously for high-throughput predictions of molecular adsorption in 3D MOFs are not suitable for 1D MOFs.

Combining Computational Screening and Machine Learning to Predict Metal-Organic Framework Adsorbents and Membranes for Removing CH4 or H2 from Air

Separating and capturing small amounts of CH4 or H2 from a mixture of gases, such as coal mine spent air, at a large scale remains a great challenge. We used large-scale computational screening and machine learning (ML) to simulate and explore the adsorption, diffusion, and permeation properties of 6013 computation-ready experimental metal-organic framework (MOF) adsorbents and MOF membranes (MOFMs) for capturing clean energy gases (CH4 and H2) in air. First, we modeled the relationships between the adsorption and the MOF membrane performance indicators and their characteristic descriptors. Among three ML algorithms, the random forest was found to have the best prediction efficiency for two systems (CH4/(O2 + N2) and H2/(O2 + N2)). Then, the algorithm was further applied to quantitatively analyze the relative importance values of seven MOF descriptors for five performance metrics of the two systems. Furthermore, the 20 best MOFs were also selected. Finally, the commonalities between the high-performance MOFs were analyzed, leading to three types of material design principles: tuned topology, alternative metal nodes, and organic linkers. As a result, this study provides microscopic insights into the capture of trace amounts of CH4 or H2 from air for applications involving coal mine spent air and hydrogen leakage.

X-ray Diffraction and Molecular Simulations in the Study of Metal-Organic Frameworks for Membrane Gas Separation

For more than a decade, researchers have been developing metal-organic frameworks (MOFs) in the form of pure MOF membranes as well as MOF-containing mixed-matrix membranes. MOF membranes have been used for H₂/CO₂ or C₃H₆/C₃H₈ separation, but relatively few MOF membranes enable the high-performance separation of CO₂/N₂, CO₂/CH₄, or N₂/CH₄. This article describes the use of in situ XRD analysis and molecular simulation to elucidate gas transport within MOFs and derivative membranes at the molecular level. In a review of recent studies by the authors and other research groups, this article examines the flexibility of MOFs initiated by activation, gas adsorption, and aging effects during gas permeation. This article also discusses the application of XRD analysis in conjunction with computational methods to investigate the CO₂-MOF Coulombic interaction and its effects on CO₂ separation. Note that this combined analysis approach is also useful in studying the effects of linker rotation on N₂/CH₄ separation. This article also examines the use of computational tools in identifying new MOFs for gas separation and, more importantly, in elaborating the relationship between the structure of MOFs and their corresponding gas transport properties.

High-throughput computational screening of hypothetical metal-organic frameworks with open copper sites for CO2/H2 separation

It is challenging to identify the optimal metal-organic framework (MOF) adsorbents for gas adsorption and membrane-based separation from the large-scale material databases. The high-throughput computational screening (HTCS) method was adopted to discover the optimal materials for CO₂/H₂ separation from thousands of MOFs. First, a hierarchical strategy was used to select 1092 MOFs from 13 512 MOFs, and their adsorption capacity towards the equimolar CO₂/H₂ mixture at 298 K and 10 bar was further calculated using the grand canonical Monte Carlo (GCMC) simulations. The results show that those MOFs with lvtb topology and organic linker 1,2,4,5-tetrazine are conducive to exhibiting high performance CO₂/H₂ adsorption separation among top-100 MOFs with high performance. The MOFs with pore limited diameter (PLD), largest cavity diameter (LCD), gravimetrical surface area (GSA), and void fraction in the range of 4-12 Å, 5-12 Å, 5500-6500 m² g^-1 and 0.80-0.85, respectively, have high adsorption capacity towards CO₂. Second, the dynamic adsorption properties of the top-4 MOFs were simulated by the breakthrough curves of the binary (CO₂/H₂) and quinary (CO₂/H₂/CH₄/CO/N₂) mixtures in the fixed adsorption bed. MOF-4641 exhibits a high breakthrough time of 130 for the quinary mixture. Finally, the adsorption mechanism of CO₂ in the top-4 MOFs was investigated by the radial distribution function (RDF), the mass center probability density distribution, etc. The atomic insights from HTCS and breakthrough curve predictions in this work will be helpful in developing novel porous materials and obtaining superior CO₂ separation performance.

Computational Screening of Metal-Organic Frameworks for Ethylene Purification from Ethane/Ethylene/Acetylene Mixture

Identification of high-performing sorbent materials is the key step in developing energy-efficient adsorptive separation processes for ethylene production. In this work, a computational screening of metal-organic frameworks (MOFs) for the purification of ethylene from the ternary ethane/ethylene/acetylene mixture under thermodynamic equilibrium conditions is conducted. Modified evaluation metrics are proposed for an efficient description of the performance of MOFs for the ternary mixture separation. Two different separation schemes are proposed and potential MOF adsorbents are identified accordingly. Finally, the relationships between the MOF structural characteristics and its adsorption properties are discussed, which can provide valuable information for optimal MOF design.

Thermal Management for Hydrogen Charging and Discharging in a Screened Metal-Organic Framework Particle Tank

Thermal management of H₂ gas storage in a tank is crucial for determining the H₂ gas deliverable capacity. In this study, a strategy for the design of an excellent comprehensive performance fuel storage tank from the screening of microscopic materials to the design of macroscopic particle adsorption tank performance is proposed. The best metal-organic framework (MOF) for H₂ deliverable capacity in a computation-ready experimental MOF database is first screened using a grand canonical Monte Carlo (GCMC) method. An upscale model that combines the finite volume method with GCMC is then established to investigate the H₂ charging and discharging processes in a screened best MOF-filled adsorption particle tank that is integrated with a phase-change material (PCM) jacket. The process of the heat and mass transfer in the screened best MOF particle adsorption tank with and without the PCM jacket-inserted metal foam is studied. The results show that the prescreened XAWVUN has the highest gravimetric and considerable volumetric deliverable capacity among 503 MOFs, which can reach up to 23.1 mol·kg^-1 and 20.8 kg·m^-3 at 298 K and pressures between 35 000 kPa (adsorption pressure) and 160 kPa (desorption pressure), respectively. The H₂ deliverable capacity can be maximized by 3.2 and 12.1% for PCM jackets inserted with metal foam in the H₂ charging and discharging processes when it is compared with the case without the PCM jacket, respectively. The above study will facilitate the development of new equipment for hydrogen storage.

Purification of Hydrogen from CO with Cu/ZSM-5 Adsorbents

The transition to a hydrogen economy requires the development of cost-effective methods for purifying hydrogen from CO. In this study, we explore the possibilities of Cu/ZSM-5 as an adsorbent for this purpose. Samples obtained by cation exchange from aqueous solution (AE) and solid-state exchange with CuCl (SE) were characterized by in situ EPR and FTIR, H₂-TPR, CO-TPD, etc. The AE samples possess mainly isolated Cu²⁺ cations not adsorbing CO. Reduction generates Cu⁺ sites demonstrating different affinity to CO, with the strongest centres desorbing CO at about 350 °C. The SE samples have about twice higher Cu/Al ratios, as one H⁺ is exchanged with one Cu⁺ cation. Although some of the introduced Cu⁺ sites are oxidized to Cu²⁺ upon contact with air, they easily recover their original oxidation state after thermal treatment in vacuum or under inert gas stream. In addition, these Cu⁺ centres regenerate at relatively low temperatures. It is important that water does not block the CO adsorption sites because of the formation of Cu⁺(CO)(H₂O)_x complexes. Dynamic adsorption studies show that Cu/ZSM-5 selectively adsorbs CO in the presence of hydrogen. The results indicate that the SE samples are very perspective materials for purification of H₂ from CO.

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.

Advanced strategies in Metal-Organic Frameworks for CO2 Capture and Separation

The continuous carbon dioxide (CO₂ ) gas emissions associated with fossil fuel production, valorization, and utilization are serious challenges to the global environment. Therefore, several developments of CO₂ capture, separation, transportation, storage, and valorization have been explored. Consequently, we documented a comprehensive review of the most advanced strategies adopted in metal-organic frameworks (MOFs) for CO₂ capture and separation. The enhancements in CO₂ capture and separation are generally achieved due to the chemistry of MOFs by controlling pore window, pore size, open-metal sites, acidity, chemical doping, post or pre-synthetic modifications. The chemistry of defects engineering, breathing in MOFs, functionalization in MOFs, hydrophobicity, and topology are the salient advanced strategies, recently reported in MOFs for CO₂ capture and separation. Therefore, this review summarizes MOF materials' advancement explaining different strategies and their role in the CO₂ mitigations. The study also provided useful insights into key areas for further investigations.

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.

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.

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

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

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.

Gating control effect facilitates excellent gas selectivity in a novel Na-SSZ-27 zeolite

A novel Na-SSZ-27 zeolite was demonstrated to possess excellent H2/CO2 diffusion selectivity of more than 100. This investigation highlights the crucial effect of the "gating control" of the 8-ring windows on the separation, where sodium cations act as gates to selectively control the diffusion of CO2 and promote the selectivity for H2.

Multiscale Computational Screening of Metal-Organic Frameworks for Kr/Xe Adsorption Separation: A Structure-Property Relationship-Based Screening Strategy

The separation of radioactive noble gases, such as Xe and Kr, has attracted special attention in the context of used nuclear fuel (UNF). In this study, 180 metal-organic frameworks (MOFs) formally used for selective adsorptions of ethane and ethylene, with a similar kinetic diameter to Kr and Xe, were initially screened for the Kr/Xe separation using the grand canonical Monte Carlo (GCMC) method. Then, the structure-adsorption property relationships were generalized, that is, the MOFs of higher Kr/Xe selectivity are with the porosity at 0.2-0.4 and the ratio of the largest cavity diameter/pore limiting diameter at 1.0-2.4. Based on the relationships, six reported MOFs with large Kr uptakes and Kr/Xe selectivities were experimentally screened out and validated by GCMC simulations within the CoRE-MOF database, which are higher than most reported MOFs under conditions pertinent to nuclear fuel reprocessing of an 80/20 v/v mixture of Kr/Xe at normal temperature and pressure. Further simulations reveal that higher Kr uptakes and Kr/Xe selectivities of six MOFs result from the confinement effect of the pores. Molecular dynamic simulations showed that the six MOFs are ideal membrane separation materials of Kr from Xe, which are driven by adsorption and diffusion. Analyses of electronic structure-based density functional theory calculations showed that the main interaction between Kr and the six MOFs is van der Waals force dominated by dispersion and induction interactions. Therefore, the generalized structure-adsorption property relationships may assist the screening of MOFs for the separation and production of Kr/Xe from UNF industrially.

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

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

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal-Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal-organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO₂. Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal-Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. A strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.