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

Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior

ConspectusNanoporous frameworks are a large and diverse family of supramolecular materials, whose chemical building units (organic, inorganic, or both) are assembled into a 3D architecture with well-defined connectivity and topology, featuring intrinsic porosity. These materials play a key role in various industrial processes and applications, such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both their physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multiscale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption.In the study of the mechanical behavior of nanoporous materials, the past few years have seen a rapid acceleration of research. For example, computational resources have been pooled to create a public large-scale database of elastic constants as part of the Materials Project initiative to accelerate innovation in materials research: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models.The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such a large scale. Tentative databases have been assembled at the DFT level on specific families of materials, such as zeolites, but prediction at larger scale currently requires the use of transferable classical force fields, whose accuracy can be limited.Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at a large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties: in particular, we describe the recent research efforts to go beyond the study of thermodynamic quantities (uptake, adsorption enthalpy, and thermodynamic selectivity) and predict transport properties using data-based methods and high-throughput computational schemes. Finally, we stress the importance of data-based methods of addressing all sources of uncertainty.The Account concludes with some perspectives about the latest developments and open questions in data-based approaches and the integration of computational and experimental data together in the materials discovery loop.

ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models

ChatMOF is an artificial intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4, GPT-3.5-turbo, and GPT-3.5-turbo-16k), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid and formal structured queries. The system is comprised of three core components (i.e., an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generations. ChatMOF shows high accuracy rates of 96.9% for searching, 95.7% for predicting, and 87.5% for generating tasks with GPT-4. Additionally, it successfully creates materials with user-desired properties from natural language. The study further explores the merits and constraints of utilizing large language models (LLMs) in combination with database and machine learning in material sciences and showcases its transformative potential for future advancements.

Metal-Organic Framework (MOF)-Based Clean Energy Conversion: Recent Advances in Unlocking its Underlying Mechanisms

Carbon neutrality is an important goal for humanity . As an eco-friendly technology, electrocatalytic clean energy conversion technology has emerged in the 21st century. Currently, metal-organic framework (MOF)-based electrocatalysis, including oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), hydrogen oxidation reaction (HOR), carbon dioxide reduction reaction (CO2RR), nitrogen reduction reaction (NRR), are the mainstream energy catalytic reactions, which are driven by electrocatalysis. In this paper, the current advanced characterizations for the analyses of MOF-based electrocatalytic energy reactions have been described in details, such as density function theory (DFT), machine learning, operando/in situ characterization, which provide in-depth analyses of the reaction mechanisms related to the above reactions reported in the past years. The practical applications that have been developed for some of the responses that are of application values, such as fuel cells, metal-air batteries, and water splitting have also been demonstrated. This paper aims to maximize the potential of MOF-based electrocatalysts in the field of energy catalysis, and to shed light on the development of current intense energy situations.

Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning

In recent data-driven approaches to material discovery, scenarios where target quantities are expensive to compute and measure are often overlooked. In such cases, it becomes imperative to construct a training set that includes the most diverse, representative, and informative samples. Here, a novel regression tree-based active learning algorithm is employed for such a purpose. It is applied to predict the band gap and adsorption properties of metal-organic frameworks (MOFs), a novel class of materials that results from the virtually infinite combinations of their building units. Simpler and low dimensional descriptors, such as those based on stoichiometric and geometric properties, are used to compute the feature space for this model owing to their ability to better represent MOFs in the low data regime. The partitions given by a regression tree constructed on the labeled part of the data set are used to select new samples to be added to the training set, thereby limiting its size while maximizing the prediction quality. Tests on the QMOF, hMOF, and dMOF data sets reveal that our method constructs small training data sets to learn regression models that predict the target properties more efficiently than existing active learning approaches, and with lower variance. Specifically, our active learning approach is highly beneficial when labels are unevenly distributed in the descriptor space and when the label distribution is imbalanced, which is often the case for real world data. The regions defined by the tree help in revealing patterns in the data, thereby offering a unique tool to efficiently analyze complex structure-property relationships in materials and accelerate materials discovery.

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m mol g-1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

Advances and Applications of Metal-Organic Frameworks (MOFs) in Emerging Technologies: A Comprehensive Review

Metal-organic frameworks (MOFs) that are the wonder material of the 21st century consist of metal ions/clusters coordinated to organic ligands to form one- or more-dimensional porous structures with unprecedented chemical and structural tunability, exceptional thermal stability, ultrahigh porosity, and a large surface area, making them an ideal candidate for numerous potential applications. In this work, the recent progress in the design and synthetic approaches of MOFs and explore their potential applications in the fields of gas storage and separation, catalysis, magnetism, drug delivery, chemical/biosensing, supercapacitors, rechargeable batteries and self-powered wearable sensors based on piezoelectric and triboelectric nanogenerators are summarized. Lastly, this work identifies present challenges and outlines future opportunities in this field, which can provide valuable references.

MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal-Organic Frameworks

The remarkable versatility of metal-organic frameworks (MOFs) stems from their rich chemical information, leading to numerous successful applications. However, identifying optimal MOFs for specific tasks necessitates a thorough assessment of their chemical attributes. Conventional machine learning approaches for MOF prediction have relied on intricate chemical and structural details, hampering rapid evaluations. Drawing inspiration from recent advancements exemplified by Snurr et al., wherein a text string was used to represent a MOF (MOFid), we introduce a MOFid-aided deep learning model, named the MOF-GRU model. This model, founded on natural language processing principles and utilizing the gated recurrent unit architecture, leverages the serialized text string representation of metal-organic frameworks (MOFs) to forecast gas separation performance. Through a focused study on CH4/N2 separation, we substantiate the efficacy of this approach. Comparative assessments against traditional machine learning techniques underscore our model's superior predictive accuracy and its capacity to handle extensive data sets adeptly. The MOF-GRU model remarkably uncovers latent structure-performance relationships with only MOF sequences, obviating the necessity for intricate three-dimensional (3D) structural information. Overall, this model's judicious design empowers efficient data utilization, thereby hastening the discovery of high-performance materials tailored for gas separation applications.

Understanding the influence of secondary building units on the thermal conductivity of metal-organic frameworks via high-throughput computational screening

The thermal conductivity of metal-organic frameworks (MOFs) has garnered increasing interest due to their potential applications in energy-related fields. However, due to the diversity of building units, understanding the relationship between MOF structures and their thermal conductivity remains an imperative challenge. In this study, we predicted the thermal conductivity (κ) of MOFs using equilibrium molecular dynamics (EMD) simulations and investigated the contribution of structure properties to their thermal conductivity. It is revealed that the arrangement of secondary building units (SBUs) with a closer distance of metal atoms, a larger proportion of metal elements, and transition metal elements (Fe, Mn, and Co) leads to high thermal conductivity. To generally quantify the influence of such factors on thermal conductivity, the pathway factors with SBU influence (Pm) were proposed and can be used to efficiently classify structures into high, medium, and low thermal conductivity types. It was found that Pm indicates that MOFs with met topology tend to have high thermal conductivity, while rna and pcu topologies naturally tend to possess medium and low thermal conductivity. Moreover, it was also suggested that taking Pm as a descriptor in the machine learning algorithms can significantly improve the prediction accuracy for thermal conductivity. This study offers molecular insight into the impact of various SBUs on thermal conductivity in framework-based nanomaterials, which may guide the rational design of nanoporous materials with desirable thermal conductivity.

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.

Transmission Porosimetry Study on High-quality Zr-fum-MOF Thin Films

Crystalline Zr-fum-MOF (MOF-801) thin films of high quality are prepared on glass and silicon substrates by direct growth under solvothermal conditions. The synthesis is described in detail and the influence of different synthesis parameters such as temperature, precursor concentration, and the substrate type on the quality of the coatings is illustrated. Zr-fum-MOF thin films are characterized in terms of crystallinity, porosity, and homogeneity. Dense films of optical quality are obtained. The sorption behavior of the thin films is studied with various adsorptives. It can be easily monitored by measuring the transmission of the films in gas flows of different compositions. This simple transmission measurement at only one wavelength allows a very fast evaluation of the adsorption properties of thin films as compared to traditional sorption methods. The sorption behavior of the thin films is compared with the sorption properties of Zr-fum-MOF powder samples.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

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.

Equation of state predictions for ScF3 and CaZrF6 with neural network-driven molecular dynamics

In silico property prediction based on density functional theory (DFT) is increasingly performed for crystalline materials. Whether quantitative agreement with experiment can be achieved with current methods is often an unresolved question, and may require detailed examination of physical effects such as electron correlation, reciprocal space sampling, phonon anharmonicity, and nuclear quantum effects (NQE), among others. In this work, we attempt first-principles equation of state prediction for the crystalline materials ScF3 and CaZrF6, which are known to exhibit negative thermal expansion (NTE) over a broad temperature range. We develop neural network (NN) potentials for both ScF3 and CaZrF6 trained to extensive DFT data, and conduct direct molecular dynamics prediction of the equation(s) of state over a broad temperature/pressure range. The NN potentials serve as surrogates of the DFT Hamiltonian with enhanced computational efficiency allowing for simulations with larger supercells and inclusion of NQE utilizing path integral approaches. The conclusion of the study is mixed: while some equation of state behavior is predicted in semiquantitative agreement with experiment, the pressure-induced softening phenomenon observed for ScF3 is not captured in our simulations. We show that NQE have a moderate effect on NTE at low temperature but does not significantly contribute to equation of state predictions at increasing temperature. Overall, while the NN potentials are valuable for property prediction of these NTE (and related) materials, we infer that a higher level of electron correlation, beyond the generalized gradient approximation density functional employed here, is necessary for achieving quantitative agreement with experiment.

A reliable QSPR model for predicting drug release rate from metal-organic frameworks: a simple and robust drug delivery approach

During the drug release process, the drug is transferred from the starting point in the drug delivery system to the surface, and then to the release medium. Metal-organic frameworks (MOFs) potentially have unique features to be utilized as promising carriers for drug delivery, due to their suitable pore size, high surface area, and structural flexibility. The loading and release of various therapeutic drugs through the MOFs are effectively accomplished due to their tunable inorganic clusters and organic ligands. Since the drug release rate percentage (RES%) is a significant concern, a quantitative structure-property relationship (QSPR) method was applied to achieve an accurate model predicting the drug release rate from MOFs. Structure-based descriptors, including the number of nitrogen and oxygen atoms, along with two other adjusted descriptors, were applied for obtaining the best multilinear regression (BMLR) model. Drug release rates from 67 MOFs were applied to provide a precise model. The coefficients of determination (R2) for the training and test sets obtained were both 0.9999. The root mean square error for prediction (RMSEP) of the RES% values for the training and test sets were 0.006 and 0.005, respectively. To examine the precision of the model, external validation was performed through a set of new observations, which demonstrated that the model works to a satisfactory degree.

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

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

Zn(II)-Based Mixed-Ligand-Bearing Coordination Polymers as Multi-Responsive Fluorescent Sensors for Detecting Dichromate, Iodide, Nitenpyram, and Imidacloprid

Coordination polymers (CPs) are organo-inorganic porous materials consisting of metal ions or clusters and organic linkers. These compounds have attracted attention for use in the fluorescence detection of pollutants. Here, two Zn-based mixed-ligand-bearing CPs, [Zn₂(DIN)₂(HBTC^2-)₂] (CP-1) and [Zn(DIN)(HBTC^2-)]·ACN·H₂O (CP-2) (DIN = 1,4-di(imidazole-1-yl)naphthalene, H₃BTC = 1,3,5-benzenetricarboxylic acid, and ACN = acetonitrile), were synthesized under solvothermal conditions. CP-1 and CP-2 were characterized by single-crystal X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric analysis, elemental analysis, and powder X-ray diffraction analysis. Solid-state fluorescence analysis revealed an emission peak at 350 nm upon excitation at 225 and 290 nm. Fluorescence sensing tests showed that CP-1 was highly efficient, sensitive, and selective for detecting Cr₂O₇^2- at 225 and 290 nm, whereas I^- was only detected well at an excitation of 225 nm. CP-1 detected pesticides differently at excitation wavelengths of 225 and 290 nm; the highest quenching rates were for nitenpyram at 225 nm and imidacloprid at 290 nm. The quenching process may occur via the inner filter effect and fluorescence resonance energy transfer.

Bismuth-Polyoxocation Coordination Networks: Controlling Nuclearity and Dimension-Dependent Photocatalysis

Bismuth-oxocluster nodes for metal-organic frameworks (MOFs) and coordination networks/polymers are less prolific than other families featuring zinc, zirconium, titanium, lanthanides, etc. However, Bi³⁺ is non-toxic, it readily forms polyoxocations, and its oxides are exploited in photocatalysis. This family of compounds provides opportunity in medicinal and energy applications. Here, we show that Bi node nuclearity depends on solvent polarity, leading to a family of Bi_x-sulfonate/carboxylate coordination networks with x = 1-38. Larger nuclearity-node networks were obtained from polar and strongly coordinating solvents, and we attribute the solvent's ability to stabilize larger species in solution. The strong role of the solvent and the lesser role of the linker in defining node topologies differ from other MOF syntheses, and this is due to the Bi³⁺ intrinsic lone pair that leads to weak node-linker interactions. We describe this family by single-crystal X-ray diffraction (eleven structures), obtained in pure forms and high yields. Ditopic linkers include NDS (1,5-naphthalenedisulfonate), DDBS (2,2'-[biphenyl-4,4'-diylchethane-2,1-diyl] dibenzenesulphonate), and NH₂-benzendicarboxylate (BDC). While the BDC and NDS linkers yield more open-framework topologies that resemble those obtained by carboxylate linkers, topologies with DDBS linkers appear to be in part driven by association between DDBS molecules. An in situ small-angle X-ray scattering study of Bi₃₈-DDBS reveals stepwise formation, including Bi₃₈-assembly, pre-organization in solution, followed by crystallization, confirming the less important role of the linker. We demonstrate photocatalytic hydrogen (H₂) generation with select members of the synthesized materials without the benefit of a co-catalyst. Band gap determination from X-ray photoelectron spectroscopy (XPS) and UV-vis data suggest the DDBS linker effectively absorbs in the visible range with ligand-to-Bi-node charge transfer. In addition, materials containing more Bi (larger Bi₃₈-nodes or Bi₆ inorganic chains) exhibit strong UV absorption, also contributing to effective photocatalysis by a different mechanism. All tested materials became black with extensive UV-vis exposure, and XPS, transmission electron microscopy, and X-ray scattering of the black Bi₃₈-framework suggest that Bi⁰ is formed in situ, without phase segregation. This evolution leads to enhanced photocatalytic performance, perhaps due to increased light absorption.

Bismuth Coordination Polymers with Fluorinated Linkers: Aqueous Stability, Bivolatility, and Adsorptive Behavior

Bismuth metal-organic frameworks and coordination polymers (CP) are challenging to synthesize, given the poor solubility of bismuth precursors and asymmetric and labile ligation of Bi³⁺ due to its intrinsic lone pair. Here, we synthesize and structurally characterize three Bi³⁺-CPs, exploiting a tetrafluoroterephtalate (F₄BDC) linker to determine the effect of high acidity on these synthesis and coordination challenges. Single-crystal X-ray diffraction characterization showed that pi-pi stacking of linkers directs framework arrangement and generally deters open porosity in the three structures, respectively featuring Bi chains (Bi _chain -F ₄ BDC), Bi dimers (Bi ₂ -F ₄ BDC) linked into chains, and Bi tetramers (Bi ₄ -F ₄ BDC). Powder X-ray diffraction and microscopic imaging show the high purity and stability of these compounds in water. Naphthalenedisulfonate (NDS) was used as a mineralizer in the synthesis of (Bi _chain -F ₄ BDC) and (Bi ₄ -F ₄ BDC), and studies of its role in assembly pathways yielded two additional structures featuring mixed NDS and F₄BDC, respectively, linking monomer and octamer Bi nodes, and confirmed that F₄BDC is the preferred (less labile) linker. Methylene blue (MB) adsorption studies show differing efficacies of the three Bi-F₄BDC phases, attributed to surface characteristics of the preferential growth facets, while generally most effective adsorption is attributed to the hydrophobicity of fluorinated ligands. Finally, thermogravimetric analysis of all three Bi-F₄BDC phases indicates simultaneous ligand degradation and in situ formation of volatile Bi compounds, which could be exploited in the chemical vapor deposition of Bi-containing thin films.

Automated Graph Neural Networks Accelerate the Screening of Optoelectronic Properties of Metal-Organic Frameworks

The numerous organic and inorganic components of metal-organic framework (MOF) materials provide intriguing optoelectronic properties. Accurately predicting the electronic structural properties of MOFs has become the main focus. This work establishes two graph neural network models, crystal graph convolutional neural networks and a materials graph network, for predicting the band gaps of more than 10 000 MOF structures and promotes to improve the prediction accuracy through automatic hyperparameter tuning algorithms. Subsequently, for exploring machine learning-assisted screening of MOFs for the broader electronic properties, the screened copper-based MOFs are compared with lead-based MAPbI₃ solar cells with respect to the band gaps, densities of states, and charge density distributions, and the results have demonstrated that the overlap of the wave functions between the initial and final states of MOFs is weakened, which is conducive to the improvement of photoelectric performance. The chlorine doping strategy further enhances the advantage. The tuning of the machine learning model and hyperparameters and the doping strategy of halogen elements furnish empirical rules for the design of MOFs with excellent optoelectronic properties.

MOFormer: Self-Supervised Transformer Model for Metal-Organic Framework Property Prediction

Metal-organic frameworks (MOFs) are materials with a high degree of porosity that can be used for many applications. However, the chemical space of MOFs is enormous due to the large variety of possible combinations of building blocks and topology. Discovering the optimal MOFs for specific applications requires an efficient and accurate search over countless potential candidates. Previous high-throughput screening methods using computational simulations like DFT can be time-consuming. Such methods also require the 3D atomic structures of MOFs, which adds one extra step when evaluating hypothetical MOFs. In this work, we propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs. MOFormer takes a text string representation of MOF (MOFid) as input, thus circumventing the need of obtaining the 3D structure of a hypothetical MOF and accelerating the screening process. By comparing to other descriptors such as Stoichiometric-120 and revised autocorrelations, we demonstrate that MOFormer can achieve state-of-the-art structure-agnostic prediction accuracy on all benchmarks. Furthermore, we introduce a self-supervised learning framework that pretrains the MOFormer via maximizing the cross-correlation between its structure-agnostic representations and structure-based representations of the crystal graph convolutional neural network (CGCNN) on >400k publicly available MOF data. Benchmarks show that pretraining improves the prediction accuracy of both models on various downstream prediction tasks. Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited. Overall, MOFormer provides a novel perspective on efficient MOF property prediction using deep learning.

Connecting the dots for fundamental understanding of structure-photophysics-property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism

Photoactive organic and hybrid organic-inorganic materials such as conjugated polymers, covalent organic frameworks (COFs), metal-organic frameworks (MOFs), and layered perovskites, display intriguing photophysical signatures upon interaction with light. Elucidating structure-photophysics-property relationships across a broad range of functional materials is nontrivial and requires our fundamental understanding of the intricate interplay among excitons (electron-hole pair), polarons (charges), bipolarons, phonons (vibrations), inter-layer stacking interactions, and different forms of structural and conformational defects. In parallel with electronic structure modeling and data-driven science that are actively pursued to successfully accelerate materials discovery, an accurate, computationally inexpensive, and physically-motivated theoretical model, which consistently makes quantitative connections with conceptually complicated experimental observations, is equally important. Within this context, the first part of this perspective highlights a unified theoretical framework in which the electronic coupling as well as the local coupling between the electronic and nuclear degrees of freedom can be efficiently described for a broad range of quasiparticles with similarly structured Holstein-style vibronic Hamiltonians. The second part of this perspective discusses excitonic and polaronic photophysical signatures in polymers, COFs, MOFs, and perovskites, and attempts to bridge the gap between different research fields using a common theoretical construct - the Multiparticle Holstein Formalism. We envision that the synergistic integration of state-of-the-art computational approaches with the Multiparticle Holstein Formalism will help identify and establish new, transformative design strategies that will guide the synthesis and characterization of next-generation energy materials optimized for a broad range of optoelectronic, spintronic, and photonic applications.

Multi-Scale Computer-Aided Design of Covalent Organic Frameworks for CO2 Capture in Wet Flue Gas

Discovery of remarkable porous materials for CO₂ capture from wet flue gas is of great significance to reduce the CO₂ emissions, but elucidating the most critical structure features for boosting CO₂ capture capabilities remains a great challenge. Here, machine-learning-assisted Monte Carlo computational screening on 516 experimental covalent organic frameworks (COFs) identifies the superior secondary building units (SBUs) for wet flue gas separation using COFs, which are tetraphenylporphyrin units for boosting CO₂ adsorption uptake and functional groups for boosting CO₂/N₂ selectivity. Accordingly, 1233 COFs are assembled using the identified superior SBUs. Density functional theory calculation analysis on frontier orbitals, electrostatic potential, and binding energy reveals the influencing mechanism of the SBUs on the wet flue gas separation performance. The "electron-donating-induced vdW interaction" effect is discovered to construct the better-performing COFs, which can achieve high CO₂ uptake of 4.4 mmol·g^-1 with CO₂/N₂ selectivity of 104.8. Meanwhile, the "electron-withdrawing-induced vdW + electrostatic coupling interaction" effect is unearthed to construct the better-performing COFs with superior CO₂/N₂ selectivity, which can reach 277.6 with CO₂ uptake of 2.2 mmol·g^-1; in this case, H₂O plays a positive contribution in improving CO₂/N₂ selectivity. This work provides useful guidelines for designing optimized two-dimensional-COF adsorbents for wet flue gas separation.

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

Saiz P, Reizabal A, Wuttke S, Celaya-Azcoaga L, Valverde A, Fernández de Luis R. A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation

Hexavalent chromium (Cr(VI)) is a highly mobile cancerogenic and teratogenic heavy metal ion. Among the varied technologies applied today to address chromium water pollution, photocatalysis offers a rapid reduction of Cr(VI) to the less toxic Cr(III). In contrast to classic photocatalysts, Metal-Organic frameworks (MOFs) are porous semiconductors that can couple the Cr(VI) to Cr(III) photoreduction to the chromium species immobilization. In this minireview, we wish to discuss and analyze the state-of-the-art of MOFs for Cr(VI) detoxification and contextualizing it to the most recent advances and strategies of MOFs for photocatalysis purposes. The minireview has been structured in three sections: (i) a detailed discussion of the specific experimental techniques employed to characterize MOF photocatalysts, (ii) a description and identification of the key characteristics of MOFs for Cr(VI) photoreduction, and (iii) an outlook and perspective section in order to identify future trends.

A Novel Instantaneous Self-Assembled Hollow MOF-Derived Nanodrug for Microwave Thermo-Chemotherapy in Triple-Negative Breast Cancer

Hollow materials derived from metal-organic frameworks (MOFs) have emerged in the biomedical field due to their unique properties, and different synthesis methods have been proposed. However, so far, the large-scale use of hollow MOFs is mostly limited by the timeliness of synthesis methods. Herein, we propose a new ultrasonic aerosol flow strategy for the instantaneous synthesis of a Zr-MOF-derived hollow sphere complex (ZC-HSC) in only one step. Through rapid transient heating, the coordination between metal salts and organic ligands occurs along with prompt evaporation of the solvent. The whole process lasts for only about 21 s, compared with several steps that take hours or even days for conventional synthesis methods. Based on the ZC-HSC, we designed a nanodrug with the functions of manipulating the tumor microenvironment, which can reshape the tumor microenvironment by improving tumor hypoxia and inflammatory microenvironment and promoting antiangiogenic therapy. Combined with microwave thermo-chemotherapy, the nanodrugs effectively treat triple-negative breast cancer (the tumor cell survival rate was only 34.76 and 31.05% in normoxic and hypoxic states, respectively, and the tumor inhibition rate reached 87.9% at the animal level), providing a new theoretical basis for the treatment of triple-negative breast cancer. This rapid, one-step, and continuous ultrasonic aerosol flow strategy has bright prospects in the synthesis of MOF-derived hollow materials and promotes the further development of large-scale applications of biological nanomaterials.

Engineering a Series of Isoreticular Pillared Layer Ultramicroporous MOFs for Gas and Vapor Uptake

The accurate design and systematic engineering of MOFs is a large challenge due to the randomness of the synthesis process. Isoreticular chemistry provides a powerful approach for the regulation of pore environment in a more predictable and precise way to systematically control gas/vapor adsorption performances. Herein, utilizing an effective strategy of altering the "pillared" motifs of pillared layer structures, three isoreticular ultramicroporous MOFs were successfully constructed. Combined with the reported parent MOFs and two other recorded isoreticular MOFs modified with -NH₂ and -CH₃, gas and vapor uptake performances of this family of isoreticular pillared layer MOFs were systematically explored.

Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials

Metal-organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms. However, the continuously increasing sizes of MOF datasets and the complicated relationships between MOFs and their gas adsorption capacities make deep learning a suitable candidate to handle such big data with increased computational power and accuracy. In this study, we developed models for predicting gas adsorption capacities of MOFs using two deep learning algorithms, multilayer perceptron (MLP) and long short-term memory (LSTM) networks, with a hypothetical set of about 130,000 structures of MOFs with methane and carbon dioxide adsorption data at different pressures. The models were evaluated using 10 iterations of 10-fold cross validations and 100 holdout validations. The MLP and LSTM models performed similarly with high prediction accuracy. The models for predicting gas adsorption at a higher pressure outperformed the models for predicting gas adsorption at a lower pressure. The deep learning models are more accurate than the random forest models reported in the literature, especially for predicting gas adsorption capacities at low pressures. Our results demonstrated that deep learning algorithms have a great potential to generate models that can accurately predict the gas adsorption capacities of MOFs.

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.

Titanium-based metal-organic framework MIL-125(Ti) for the highly selective isolation and purification of immunoglobulin G from human serum

Titanium-based metal-organic framework MIL-125(Ti) was synthesized by the hydrothermal method of terephthalic acid and tetra butyl titanate in N-N dimethylformamide and methanol. MIL-125(Ti) was characterized by Fourier transform infrared spectroscopy, X-ray diffraction, thermogravimetric analysis, nitrogen adsorption-desorption, energy-dispersive X-ray spectroscopy, zeta potential, scanning electron microscope and transmission electron microscopy. The results showed MIL-125(Ti) could be used as a potential adsorbent for protein separation and purification due to the high specific surface area, high stability and strong hydrophobicity. As a result, MIL-125(Ti) had adsorption selectivity for immunoglobulin G, which was due to hydrogen bond between MIL-125(Ti) and protein. At pH 8.0, the maximum adsorption efficiency of 0.25 mg MIL-125(Ti) for 300 μL 100 μg mL^-1 immunoglobulin G was 98.3%, and its maximum adsorption capacity was 232.56 mg g^-1 . The elution efficiency of immunoglobulin G was 92.4% by 0.1% SDS. SDS-PAGE result demonstrated the successful isolation of highly purified immunoglobulin G from the human serum. Therefore, a new method of separation and purification of immunoglobulin G in human serum using titanium-based metal-organic framework MIL-125(Ti) as a solid-phase adsorbent was established, which broadened the application scope of metal-organic frameworks. This article is protected by copyright. All rights reserved.

Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs

Due to the enormous increase in the number of metal-organic frameworks (MOFs), combining molecular simulations with machine learning (ML) would be a very useful approach for the accurate and rapid assessment of the separation performances of thousands of materials. In this work, we combined these two powerful approaches, molecular simulations and ML, to evaluate MOF membranes and MOF/polymer mixed matrix membranes (MMMs) for six different gas separations: He/H₂, He/N₂, He/CH₄, H₂/N₂, H₂/CH₄, and N₂/CH₄. Single-component gas uptakes and diffusivities were computed by grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, respectively, and these simulation results were used to assess gas permeabilities and selectivities of MOF membranes. Physical, chemical, and energetic features of MOFs were used as descriptors, and eight different ML models were developed to predict gas adsorption and diffusion properties of MOFs. Gas permeabilities and membrane selectivities of 5249 MOFs and 31,494 MOF/polymer MMMs were predicted using these ML models. To examine the transferability of the ML models, we also focused on computer-generated, hypothetical MOFs (hMOFs) and predicted the gas permeability and selectivity of 1000 hMOF/polymer MMMs. The ML models that we developed accurately predict the uptake and diffusion properties of He, H₂, N₂, and CH₄ gases in MOFs and will significantly accelerate the assessment of separation performances of MOF membranes and MOF/polymer MMMs. These models will also be useful to direct the extensive experimental efforts and computationally demanding molecular simulations to the fabrication and analysis of membrane materials offering high performance for a target gas separation.

Application of Fiber Biochar-MOF Matrix Composites in Electrochemical Energy Storage

Fiber biochar–metal organic framework (MOF) composites were successfully prepared by three different biochar preparation methods, namely, the ionic liquid method, the pyrolysis method, and the direct composite method. The effects of the different preparation methods of fiber biochar on the physical and chemical properties of the biochar–MOF composites showed that the composite prepared by the ionic liquid method with the Zeolite-type imidazolate skeleton -67 (ZIF-67) composite after high temperature treatment exhibited a better microstructure. Electrochemical tests showed that it had good specific capacity, a fast charge diffusion rate, and a relatively good electrochemical performance. The maximum specific capacity of the composite was 63.54 F/g when the current density was 0.01 A/g in 1 mol/L KCl solution. This work explored the preparation methods of fiber biochar–MOF composites and their application in the electrochemical field and detailed the relationship between the preparation methods of the composites and the electrochemical properties of the electrode materials.

Metal-Organic Framework-Based Materials for Adsorption and Detection of Uranium(VI) from Aqueous Solution

The steady supply of uranium resources and the reduction or elimination of the ecological and human health hazards of wastewater containing uranium make the recovery and detection of uranium in water greatly important. Thus, the development of effective adsorbents and sensors has received growing attention. Metal-organic frameworks (MOFs) possessing fascinating characteristics such as high surface area, high porosity, adjustable pore size, and luminescence have been widely used for either uranium adsorption or sensing. Now pertinent research has transited slowly into simultaneous uranium adsorption and detection. In this review, the progress on the research of MOF-based materials used for both adsorption and detection of uranium in water is first summarized. The adsorption mechanisms between uranium species in aqueous solution and MOF-based materials are elaborated by macroscopic batch experiments combined with microscopic spectral technology. Moreover, the application of MOF-based materials as uranium sensors is focused on their typical structures, sensing mechanisms, and the representative examples. Furthermore, the bifunctional MOF-based materials used for simultaneous detection and adsorption of U(VI) from aqueous solution are introduced. Finally, we also discuss the challenges and perspectives of MOF-based materials for uranium adsorption and detection to provide a useful inspiration and significant reference for further developing better adsorbents and sensors for uranium containment and detection.

Electrochemical activation of CO2 by MOF-(Fe, Ni, Mn) derivatives of 5-aminoisophthalic acid and the thiazole group influence on its catalytic activity

The electrocatalysis of carbon dioxide using earth abundant metals and a functionalized MOF to find a sustainable process was investigated.

Nanochannel-Based {BaZn}-Organic Framework for Catalytic Activity on Cycloaddition Reaction of Epoxides with CO2 and Deacetalization-Knoevenagel Condensation

Because of the integrated properties from chemically dissimilar metals, microporous heterometallic MOFs have wider potential applicability, which prompts us to explore the tendency collocation of different metal cations in the...

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

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