RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials

Adsorption-driven reverse osmosis separation of ethanol/water using zeolite nanosheets

Developing more energy-efficient and cost-effective membrane processes for the separation of ethanol and water represents a strategically important direction to facilitate the production of renewable biofuels. In this study, by employing state-of-the-art molecular simulations, the potential of zeolite nanosheets as reverse osmosis (RO) membranes in ethanol/water separation is investigated. These materials are predicted to offer unprecedentedly high fluxes and more importantly, the ethanol-to-water separation factor can be as large as approximately 800 if the structure is meticulously selected. The separation achieved herein can in fact be considered counter-intuitive as the membrane allows the larger ethanol molecules to permeate through while blocking smaller water molecules. Further investigations reveal that the observed selectivity is strongly correlated with the adsorption selectivity of the bulk materials, suggesting an adsorption-driven mechanism. Promising candidates also appear to have the largest cavity diameter of approximately 6 Å, a size that can be commensurate with the dimensions of ethanol to facilitate its adsorption. The hydrophilicity on the membrane surfaces is as well found to play a non-negligible role. Overall, this study demonstrates the great promise of zeolite nanosheets as RO membranes for extracting anhydrous ethanol from its aqueous mixture and provides guidance toward the selection of promising membrane candidates.

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

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

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.

Adsorption in Pyrene-Based Metal-Organic Frameworks: The Role of Pore Structure and Topology

Pore topology and chemistry play crucial roles in the adsorption characteristics of metal-organic frameworks (MOFs). To deepen our understanding of the interactions between MOFs and CO2 during this process, we systematically investigate the adsorption properties of a group of pyrene-based MOFs. These MOFs feature Zn(II) as the metal ion and employ a pyrene-based ligand, specifically 1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy). Including different additional ligands leads to frameworks with distinctive structural and chemical features. By comparing these structures, we could isolate the role that pore size, the presence of open-metal sites (OMS), metal-oxygen bridges, and framework charges play in the CO2 adsorption of these MOFs. Frameworks with constricted pore structures display a phenomenon known as the confinement effect, fostering stronger MOF-CO2 interactions and higher uptakes at low pressures. In contrast, entropic effects dominate at elevated pressures, and the MOF's pore volume becomes the driving factor. Through analysis of the CO2 uptakes of the benchmark materials ─some with narrower pores and others with larger pore volumes─it becomes evident that structures with narrower pores and high binding energies excel at low pressures. In contrast, those with larger volumes perform better at elevated pressures. Moreover, this research highlights that open-metal sites and inherent charges within the frameworks of ionic MOFs stand out as CO2-philic characteristics.

Tailoring parameters for QM/MM simulations: accurate modeling of adsorption and catalysis in zirconium-based metal-organic frameworks

Quantum mechanics/molecular mechanics (QM/MM) simulations offer an efficient way to model reactions occurring in complex environments. This study introduces a specialized set of charge and Lennard-Jones parameters tailored for electrostatically embedded QM/MM calculations, aiming to accurately model both adsorption processes and catalytic reactions in zirconium-based metal-organic frameworks (Zr-MOFs). To validate our approach, we compare adsorption energies derived from QM/MM simulations against experimental results and Monte Carlo simulation outcomes. The developed parameters showcase the ability of QM/MM simulations to represent long-range electrostatic and van der Waals interactions faithfully. This capability is evidenced by the prediction of adsorption energies with a low root mean square error of 1.1 kcal mol-1 across a wide range of adsorbates. The practical applicability of our QM/MM model is further illustrated through the study of glucose isomerization and epimerization reactions catalyzed by two structurally distinct Zr-MOF catalysts, UiO-66 and MOF-808. Our QM/MM calculations closely align with experimental activation energies. Importantly, the parameter set introduced here is compatible with the widely used universal force field (UFF). Moreover, we thoroughly explore how the size of the cluster model and the choice of density functional theory (DFT) methodologies influence the simulation outcomes. This work provides an accurate and computationally efficient framework for modeling complex catalytic reactions within Zr-MOFs, contributing valuable insights into their mechanistic behaviors and facilitating further advancements in this dynamic area of research.

Unraveling the Influence of Nafion Content on the Performance of Proton-Exchange Membrane Fuel Cells from the Perspective of Triple-Phase Boundary

By combining molecular simulations and experimental measurements, the effect of the Nafion content on the performance of proton-exchange membrane fuel cells (PEMFCs) is explained from the perspective of the triple-phase boundary (TPB). The evaporation process of Nafion solvent is simulated on a triple-phase model to mimic the formation of the TPB, and the influence of the Nafion content on the TPB structure is investigated. When the Nafion content is 1.415 mg/m2, the coverages of Nafion on both Pt particles and the carbon carrier are saturated at 42.1% and 32.7%, respectively. With the increase of Nafion content, the amount of water molecules around Pt particles is increased, and the surrounding O2 content is decreased. The experimental PEMFC performance has confirmed such simulation results, which demonstrates a trend of enhancing first and then weakening with the increase of Nafion content and reaches a maximum with the Nafion content of 2.96 mg/m2. Therefore, the correlation between the structure of the TPB and the cell's efficiency has been established at a molecular level, enabling enhancements in the design of the TPB morphology and an increase in PEMFC efficiency.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

An automated protocol to construct flexibility parameters for classical forcefields: applications to metal-organic frameworks

In this work, forcefield flexibility parameters were constructed and validated for more than 100 metal-organic frameworks (MOFs). We used atom typing to identify bond types, angle types, and dihedral types associated with bond stretches, angle bends, dihedral torsions, and other flexibility interactions. Our work used Manz's angle-bending and dihedral-torsion model potentials. For a crystal structure containing N atoms in its unit cell, the number of independent flexibility interactions is 3(N atoms - 1). Because the number of bonds, angles, and dihedrals is normally much larger than 3(N atoms - 1), these internal coordinates are redundant. To reduce (but not eliminate) this redundancy, our protocol prunes dihedral types in a way that preserves symmetry equivalency. Next, each dihedral type is classified as non-rotatable, hindered, rotatable, or linear. We introduce a smart selection method that identifies which particular torsion modes are important for each rotatable dihedral type. Then, we computed the force constants for all flexibility interactions together via LASSO regression (i.e., regularized linear least-squares fitting) of the training dataset. LASSO automatically identifies and removes unimportant forcefield interactions. For each MOF, the reference dataset was quantum-mechanically-computed in VASP via DFT with dispersion and included: (i) finite-displacement calculations along every independent atom translation mode, (ii) geometries randomly sampled via ab initio molecular dynamics (AIMD), (iii) the optimized ground-state geometry using experimental lattice parameters, and (iv) rigid torsion scans for each rotatable dihedral type. After training, the flexibility model was validated across geometries that were not part of the training dataset. For each MOF, we computed the goodness of fit (R-squared value) and the root-mean-squared error (RMSE) separately for the training and validation datasets. We compared flexibility models with and without bond-bond cross terms. Even without cross terms, the model yielded R-squared values of 0.910 (avg across all MOFs) ± 0.018 (st. dev.) for atom-in-material forces in the validation datasets. Our SAVESTEPS protocol should find widespread applications to parameterize flexible forcefields for material datasets. We performed molecular dynamics simulations using these flexibility parameters to compute heat capacities and thermal expansion coefficients for two MOFs.

Controlling the Uptake and Release of Semiochemicals in Channel-Type Metal-Organic Frameworks Through Pore Expansion

Semiochemicals can be used to manipulate insect behaviour for sustainable pest management strategies, but their high volatility is a major issue for their practical implementation. Inclusion of these molecules within porous materials is a potential solution to this issue, as it can allow for a slower and more controlled release. In this work, we demonstrate that a series of Zr(IV) and Al(III) metal-organic frameworks (MOFs) with channel-type pores enable controlled release of three semiochemicals over 100 days by pore size design, with the uptake and rate of release highly dependent on the pore size. Insight from grand canonical Monte Carlo simulations indicates that this is due to weaker MOF-guest interactions per guest molecule as the pore size increases. These MOFs are all stable post-release and can be reloaded to show near-identical re-release profiles. These results provide valuable insight on the diffusion behaviour of volatile guests in MOFs, and for the further development of porous materials for sustainable agriculture applications.

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

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

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

Simulation study on functional group-modified Ni-MOF-74 for CH4/N2 adsorption separation

This study employs grand canonical Monte Carlo (GCMC) simulations to investigate the impact of functional group modifications (CH3, OH, NH2, and OLi) on the adsorption performance of CH4/N2 on Ni-MOF-74. The results revealed that functional group modifications significantly increased the adsorption capacity of Ni-MOF-74 for both CH4 and N2. The packed methyl groups in CH3-Ni-MOF-74 create an environment conducive to CH4, leading to the highest CH4 adsorption capacity. The electrostatic potential distribution indicates that the strong electron-donating effect introduced by the alkali metal Li results in the highest electrostatic potential gradient in Li-O-Ni-MOF-74, leading to the strongest adsorption of N2, this is unfavorable for CH4/N2 separation. At 1500 kPa the selectivity order of adsorbents for mixed gases was as follows: CH3-Ni-MOF-74 > NH2-Ni-MOF-74 > OH-Ni-MOF-74 > Ni-MOF-74 > Li-O-Ni-MOF-74. This study highlights that CH3-Ni-MOF-74 possesses optimal CH4 selectivity and adsorption performance. Given the current lack of research on functionalized MOF-74 for the separation of CH4 and N2, the findings of this study will serve as a theoretical guide and provide references for the applications of CH4 adsorption and CH4/N2 separation.

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

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

Reaching Quantum Accuracy in Predicting Adsorption Properties for Ethane/Ethene in Zeolitic Imidazolate Framework-8 at Low Pressure Regime

Nanoporous materials in the form of metal-organic frameworks such as zeolitic imidazolate framework-8 (ZIF-8) are promising membrane materials for the separation of hydrocarbon mixtures. To compute the adsorption isotherms in such adsorbents, grand canonical Monte Carlo simulations have proven to be very useful. The quality of these isotherms depends on the accuracy of adsorbate-adsorbent interactions, which are mostly described using force fields owing to their low computational cost. However, force field predictions of adsorption uptake often show discrepancies from experiments at low pressures, providing the need for methods that are more accurate. Hence, in this work, we propose and validate two novel methodologies for the ZIF-8/ethane and ethene systems; a benchmarking methodology to evaluate the performance of any given force field in describing adsorption in the low-pressure regime and a refinement procedure to rescale the parameters of a force field to better describe the host-guest interactions and provide for simulation isotherms with close agreement to experimental isotherms. Both methodologies were developed based on a reference Henry coefficient, computed with the PBE-MBD functional using the importance sampling technique. The force field rankings predicted by the benchmarking methodology involve the comparison of force field derived Henry coefficients with the reference Henry coefficients and ranking the force fields based on the disparities between these Henry coefficients. The ranking from this methodology matches the rankings made based on uptake disparities by comparing force field derived simulation isotherms to experimental isotherms in the low-pressure regime. The force field rescaling methodology was proven to refine even the worst performing force field in UFF/TraPPE. The uptake disparities of UFF/TraPPE improved from 197% and 194% to 11% and 21% for ethane and ethene, respectively. The proposed methodology is applicable to predict adsorption across nanoporous materials and allows for rescaled force fields to reach quantum accuracy without the need for experimental input.

Data-Driven and Machine Learning to Screen Metal-Organic Frameworks for the Efficient Separation of Methane

With the rapid growth of the economy, people are increasingly reliant on energy sources. However, in recent years, the energy crisis has gradually intensified. As a clean energy source, methane has garnered widespread attention for its development and utilization. This study employed both large-scale computational screening and machine learning to investigate the adsorption and diffusion properties of thousands of metal-organic frameworks (MOFs) in six gas binary mixtures of CH4 (H2/CH4, N2/CH4, O2/CH4, CO2/CH4, H2S/CH4, He/CH4) for methane purification. Firstly, a univariate analysis was conducted to discuss the relationships between the performance indicators of adsorbents and their characteristic descriptors. Subsequently, four machine learning methods were utilized to predict the diffusivity/selectivity of gas, with the light gradient boosting machine (LGBM) algorithm emerging as the optimal one, yielding R2 values of 0.954 for the diffusivity and 0.931 for the selectivity. Furthermore, the LGBM algorithm was combined with the SHapley Additive exPlanation (SHAP) technique to quantitatively analyze the relative importance of each MOF descriptor, revealing that the pore limiting diameter (PLD) was the most critical structural descriptor affecting molecular diffusivity. Finally, for each system of CH4 mixture, three high-performance MOFs were identified, and the commonalities among high-performance MOFs were analyzed, leading to the proposals of three design principles involving changes only to the metal centers, organic linkers, or topological structures. Thus, this work reveals microscopic insights into the separation mechanisms of CH4 from different binary mixtures in MOFs.

Molecular fingerprint and machine learning enhance high-performance MOFs for mustard gas removal

Chemical warfare agents (CWAs), epitomized by the notoriously used mustard gas (HD), represent a class of exceptionally toxic chemicals whose airborne removal is paramount for battlefield safety. This study integrates high-throughput computational screening (HTCS) with advanced machine learning (ML) techniques to investigate the efficacy of metal-organic frameworks (MOFs) in adsorbing and capturing trace amounts of HD present in the air. Our approach commenced with a comprehensive univariate analysis, scrutinizing the impact of six distinct descriptors on the adsorption efficiency of MOFs. This analysis elucidated a pronounced correlation between MOF density and the Henry coefficient in the effective capture of HD. Then, four ML algorithms were employed to train and predict the performance of MOFs. The Random Forest (RF) algorithm demonstrates strong model learning and good generalization, achieving the best prediction result of 98.3%. In a novel exploratory stride, we incorporated a 166-bit MACCS molecular fingerprinting (MF) to identify critical functional groups within adsorbents. From the top 100 MOFs analyzed, 22 optimal functional groups were identified. Leveraging these insights, we designed three innovative substructures, grounded in these key functional groups, to enhance HD adsorption efficiency. In this work, the combination of MF and ML could provide a new direction for efficient screening of MOFs for the capture of HD in the air. The outcomes of this study offer substantial potential to revolutionize the domain of CWA capture. This represents a significant stride toward developing practical solutions that enhance both environmental protection and battlefield security.

Hydrogen storage in M(BDC)(TED)0.5 metal-organic framework: physical insights and capacities

Finding renewable energy sources to replace fossil energy has been an essential demand in recent years. Hydrogen gas has been becoming a research hotspot for its clean and free-carbon energy. However, hydrogen storage technology is challenging for mobile and automotive applications. Metal-organic frameworks (MOFs) have emerged as one of the most advanced materials for hydrogen storage due to their exceptionally high surface area, ultra-large and tuneable pore size. Recently, computer simulations allowed the designing of new MOF structures with significant hydrogen storage capacity. However, no studies are available to elucidate the hydrogen storage in M(BDC)(TED)0.5, where M = metal, BDC = 1,4-benzene dicarboxylate, and TED = triethylenediamine. In this report, we used van der Waals-dispersion corrected density functional theory and grand canonical Monte Carlo methods to explore the electronic structure properties, adsorption energies, and gravimetric and volumetric hydrogen loadings in M(BDC)(TED)0.5 (M = Mg, V, Co, Ni, and Cu). Our results showed that the most favourable adsorption site of H2 in M(BDC)(TED)0.5 is the metal cluster-TED intersection region, in which Ni offers the strongest binding strength with the adsorption energy of -16.9 kJ mol-1. Besides, the H2@M(BDC)(TED)0.5 interaction is physisorption, which mainly stems from the contribution of the d orbitals of the metal atoms for M = Ni, V, Cu, and Co and the p orbitals of the O, C, N atoms for M = Mg interacting with the σ* state of the adsorbed hydrogen molecule. Noticeably, the alkaline-earth metal Mg strongly enhanced the specific surface area and pore size of the M(BDC)(TED)0.5 MOF, leading to an enormous increase in hydrogen storage with the highest absolute (excess) gravimetric and volumetric uptakes of 1.05 (0.36) wt% and 7.47 (2.59) g L-1 at 298 K and 7.42 (5.80) wt% and 52.77 (41.26) g L-1 at 77 K, respectively. The results are comparable to the other MOFs found in the literature.

Quantifying the Uncertainty of Force Field Selection on Adsorption Predictions in MOFs

Comparisons between simulated and experimental adsorption isotherms in MOFs are fraught with challenges. On the experimental side, there is significant variation between isotherms measured on the same system, with a significant percentage (∼20%) of published data being considered outliers. On the simulation side, force fields are often chosen "off-the-shelf" with little or no validation. The effect of this choice on the reliability of simulated adsorption predictions has not yet been rigorously quantified. In this work, we fill this gap by systematically quantifying the uncertainty arising from force field selection on adsorption isotherm predictions. We choose methane adsorption, where electrostatic interactions are negligible, to independently study the effect of the framework Lennard-Jones parameters on a series of prototypical materials that represent the most widely studied MOF "families". Using this information, we compute an adsorption "consensus isotherm" from simulations, including a quantification of uncertainty, and compare it against a manually curated set of experimental data from the literature. By considering many experimental isotherms measured by different groups and eliminating outliers in the data using statistical analysis, we conduct a rigorous comparison that avoids the pitfalls of the standard approach of comparing simulation predictions to a single experimental data set. Our results show that (1) the uncertainty in simulated isotherms can be as large as 15% and (2) standard force fields can provide reliable predictions for some systems but can fail dramatically for others, highlighting systematic shortcomings in those models. Based on this, we offer recommendations for future simulation studies of adsorption, including high-throughput computational screening of MOFs.

Understanding the origins of reversible and hysteretic pathways of adsorption phase transitions in metal-organic frameworks

Phase behavior of nanoconfined fluids adsorbed in metal-organic frameworks is of paramount importance for the design of advanced materials for energy and gas storage, separations, electrochemical devices, sensors, and drug delivery, as well as for the pore structure characterization. Phase transformations in adsorbed fluids often involve long-lasting metastable states and hysteresis that has been well-documented in gas adsorption-desorption and nonwetting fluid intrusion-extrusion experiments. However, theoretical prediction of the observed nanophase behavior remains a challenging problem. The mesoscopic canonical, or mesocanonical, ensemble (MCE) is devised to study the nanophase behavior under conditions of controlled fluctuations to stabilize metastable and labile states. Here, we implement and apply the MCE Monte Carlo (MCEMC) simulation scheme to predict the origins of reversible and hysteric adsorption phase transitions in a series of practical MOF materials, including IRMOF-1, ZIF-412, UiO-66, Cu-BTC, IRMOF-74-V, VII, and IX. The MCEMC method, called the gauge cell method, allows to produce Van der Waals type isotherms with distinctive swings around the phase transition regions. The constructed isotherms determine the positions of phase equilibrium and spinodals, as well as the nucleation barriers separating metastable states. We demonstrate the unique capabilities of the MCEMC method in quantitative predictions of experimental observations compared with the conventional grand canonical and canonical ensemble simulations. The MCEMC method is implemented in the open-source RASPA and LAMMPS software packages and recommended for studies of adsorption behavior and pore structure characterization of MOFs and other nanoporous materials.

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.

Coarse-grained force field for ZIF-8: A study on adsorption, diffusion, and structural properties

Metal-organic frameworks (MOFs) are revolutionizing a spectrum of industries, from groundbreaking gas storage solutions to transformative biological system applications. The intricate architecture of these materials necessitates the use of advanced computational techniques for a comprehensive understanding of their molecular structure and prediction of their physical properties. Coarse-grained (CG) simulations shine a spotlight on the often-neglected influences of defects, pressure effects, and spatial disorders on the performance of MOFs. These simulations are not just beneficial but indispensable for high-demand applications, such as mixed matrix membranes and intricate biological system interfaces. In this work, we propose an optimized CG force field tailored for ZIF-8. Our work provides a deep dive into sorption isotherms and diffusion coefficients of small molecules. We demonstrate the structural dynamics of ZIF-8, particularly how it responds to pressurization, which affects its crystal structure and leads to local changes in aperture size and area. Emphasizing the game-changing potential of CG simulations, we explore the characteristics of amorphization in ZIF-8. Through computational exploration, we aim to bridge the knowledge gap, enhancing the potential applications of nanoporous materials for various applications.

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture

Direct air capture (DAC) of CO2 with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H2O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.

Deep learning and big data mining for Metal-Organic frameworks with high performance for simultaneous desulfurization and carbon capture

Carbon capture and desulfurization of flue gases are crucial for the achievement of carbon neutrality and sustainable development. In this work, the "one-step" adsorption technology with high-performance metal-organic frameworks (MOFs) was proposed to simultaneously capture the SO2 and CO2. Four machine learning algorithms were used to predict the performance indicators (NCO2+SO2, SCO2+SO2/N2, and TSN) of MOFs, with Multi-Layer Perceptron Regression (MLPR) showing better performance (R2 = 0.93). To address sparse data of MOF chemical descriptors, we introduced the Deep Factorization Machines (DeepFM) model, outperforming MLPR with a higher R2 of 0.95. Then, sensitivity analysis was employed to find that the adsorption heat and porosity were the key factors for SO2 and CO2 capture performance of MOF, while the influence of open alkali metal sites also stood out. Furthermore, we established a kinetic model to batch simulate the breakthrough curves of TOP 1000 MOFs to investigate their dynamic adsorption separation performance for SO2/CO2/N2. The TOP 20 MOFs screened by the dynamic performance highly overlap with those screened by the static performance, with 76 % containing open alkali metal sites. This integrated approach of computational screening, machine learning, and dynamic analysis significantly advances the development of efficient MOF adsorbents for flue gas treatment.

Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo

With the specter of accelerating climate change, securing access to potable water has become a critical global challenge. Atmospheric water harvesting (AWH) through metal-organic frameworks (MOFs) emerges as one of the promising solutions. The standard numerical methods applied for rapid and efficient screening for optimal sorbents face significant limitations in the case of water adsorption (slow convergence and inability to overcome high energy barriers). To address these challenges, we employed grand canonical transition matrix Monte Carlo (GC-TMMC) methodology and proposed an efficient interpolation scheme that significantly reduces the number of required simulations while maintaining accuracy of the results. Through the example of water adsorption in three MOFs: MOF-303, MOF-LA2-1, and NU-1000, we show that the extrapolation of the free energy landscape allows for prediction of the adsorption properties over a continuous range of pressure and temperature. This innovative and versatile method provides rich thermodynamic information, enabling rapid, large-scale computational screening of sorbents for adsorption, applicable for a variety of sorbents and gases. As the presented methodology holds strong applicative potential, we provide alongside this paper a modified version of the RASPA2 code with a ghost swap move implementation and a Python library designed to minimize the user's input for analyzing data derived from the TMMC simulations.

Avoiding pitfalls in molecular simulation of vapor sorption: Example of propane and isobutane in metal-organic frameworks for adsorption cooling applications

This study introduces recommendations for conducting molecular simulations of vapor adsorption, with an emphasis on enhancing the accuracy, reproducibility, and comparability of results. The first aspect we address is consistency in the implementation of some details of typical molecular models, including tail corrections and cutoff distances, due to their significant influence on generated data. We highlight the importance of explicitly calculating the saturation pressures at relevant temperatures using methods such as Gibbs ensemble Monte Carlo simulations and illustrate some pitfalls in extrapolating saturation pressures using this method. For grand canonical Monte Carlo (GCMC) simulations, the input fugacity is usually calculated using an equation of state, which often requires the critical parameters of the fluid. We show the importance of using critical parameters derived from the simulation with the same model to ensure internal consistency between the simulated explicit adsorbate phase and the implicit bulk phase in GCMC. We show the advantages of presenting isotherms on a relative pressure scale to facilitate easier comparison among models and with experiment. Extending these guidelines to a practical case study, we evaluate the performance of various isoreticular metal-organic frameworks (MOFs) in adsorption cooling applications. This includes examining the advantages of using propane and isobutane as working fluids and identifying MOFs with a superior performance.

A Copper-Based Metal-Organic Framework for Selective Separation of C2 Hydrocarbons from Methane at Ambient Conditions: Experiment and Simulation

C2 hydrocarbon separation from methane represents a technological challenge for natural gas upgrading. Herein, we report a new metal-organic framework, [Cu2L(DEF)2]·2DEF (UNT-14; H4L = 4,4',4″,4‴-((1E,1'E,1″E,1‴E)-benzene-1,2,4,5-tetrayltetrakis(ethene-2,1-diyl))tetrabenzoic acid; DEF = N,N-diethylformamide; UNT = University of North Texas). The linker design will potentially increase the surface area and adsorption energy owing to π(hydrocarbon)-π(linker)/M interactions, hence increasing C2 hydrocarbon/CH4 separation. Crystallographic data unravel an sql topology for UNT-14, whereby [Cu2(COO)4]···[L]4- paddle-wheel units afford two-dimensional porous sheets. Activated UNT-14a exhibits moderate porosity with an experimental Brunauer-Emmett-Teller (BET) surface area of 480 m2 g-1 (vs 1868 m2 g-1 from the crystallographic data). UNT-14a exhibits considerable C2 uptake capacity under ambient conditions vs CH4. GCMC simulations reveal higher isosteric heats of adsorption (Qst) and Henry's coefficients (KH) for UNT-14a vs related literature MOFs. Ideal adsorbed solution theory yields favorable adsorption selectivity of UNT-14a for equimolar C2Hn/CH4 gas mixtures, attaining 31.1, 11.9, and 14.8 for equimolar mixtures of C2H6/CH4, C2H4/CH4, and C2H2/CH4, respectively, manifesting efficient C2 hydrocarbon/CH4 separation. The highest C2 uptake and Qst being for ethane are also desirable technologically; it is attributed to the greatest number of "agostic" or other dispersion C-H bond interactions (6) vs 4/2/4 for ethylene/acetylene/methane.

Gas adsorption and framework flexibility of CALF-20 explored via experiments and simulations

In 2021, Svante, in collaboration with BASF, reported successful scale up of CALF-20 production, a stable MOF with high capacity for post-combustion CO2 capture which exhibits remarkable stability towards water. CALF-20's success story in the MOF commercialisation space provides new thinking about appropriate structural and adsorptive metrics important for CO2 capture. Here, we combine atomistic-level simulations with experiments to study adsorptive properties of CALF-20 and shed light on its flexible crystal structure. We compare measured and predicted CO2 and water adsorption isotherms and explain the role of water-framework interactions and hydrogen bonding networks in CALF-20's hydrophobic behaviour. Furthermore, regular and enhanced sampling molecular dynamics simulations are performed with both density-functional theory (DFT) and machine learning potentials (MLPs) trained to DFT energies and forces. From these simulations, the effects of adsorption-induced flexibility in CALF-20 are uncovered. We envisage this work would encourage development of other MOF materials useful for CO2 capture applications in humid conditions.

Pore Structure Compartmentalization for Advanced Characterization of Metal-Organic Framework Materials

Metal-organic frameworks (MOFs) are nanoporous crystals which are widely used as selective adsorbents, separation membranes, catalysts, gas and energy storage media, and drug delivery vehicles. The unique adsorption and transport properties of MOFs are determined by their complex three-dimensional (3D) networks of pores, cages, and channels that differ in size, shape, and chemical composition. While the morphological structure of MOF crystals is known, practical MOF materials are rarely ideal crystals. They contain secondary phases, binders, residual chemicals, and various types of defects. It is of paramount importance to evaluate the degree of crystallinity and accessibility of different pore compartments to adsorb guest molecules. To this end, we recently suggested the method of fingerprint isotherms based on the comparison of the experimentally measured adsorption isotherms and theoretical isotherms on ideal MOF crystals produced by Monte Carlo (MC) simulations and decomposed with respect to different pore compartments [Parashar, S. ACS Appl. Nano Mater. 2021, 4, 5531-5540 and Dantas, S.; Neimark, A. V. ACS Appl. Mater. Interfaces 2020, 12, 15595-15605]. In this work, we develop an automated algorithm for pore network compartmentalization that is a prerequisite for calculations of the fingerprint isotherms. The proposed algorithm partitions the unit cell into realistically shaped compartments based on the geometric pore size distribution. The proposed method is demonstrated on several characteristic systems, including Cu-BTC, IRMOF-1, UiO-66, PCN-224, ZIF-412, and 56 structures from the CoRE MOF database.

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

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

Diversity in Atomic Structures of Zeolite-Templated Carbons and the Consequences for Macroscopic Properties

Zeolite-templated carbons (ZTCs) are a family of ordered microporous carbons with extralarge surface areas and micropore volumes, which are synthesized by carbon deposition within the confined spaces of zeolite micropores. There has been great controversy regarding the atomic structures of ZTCs, which encompass two extremes: (1) three-dimensionally connected curved open-blade-type carbon moieties and (2) ideal tubular structures (commonly referred to as "Schwarzites"). In this study, through a combination of experimental analyses and theoretical calculations, we demonstrate that the atomic structure of ZTCs is difficult to define as a single entity, and it widely varies depending on their synthesis conditions. Carbon deposition using a large organic precursor and low-temperature framework densification generates ZTCs predominantly composed of open-blade-type moieties, characterized by low surface curvature and abundant H-terminated edge sites. Meanwhile, synthesis using a small precursor with high-temperature densification produces ZTCs with an increased portion of closed-strut carbon moieties (or closed-fullerene-like nodes), exhibiting large surface curvature and diminished edge sites. The variations in the atomic structure of ZTCs result in significant differences in their macroscopic properties, such as N2/CO2 adsorption, oxidative stability, work function, and electrocatalytic properties, despite the presence of comparable pore structures. Therefore, ZTCs demonstrate the potential to synthesize ordered nanoporous carbons with tunable physicochemical properties.

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

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

High throughput screening of pure silica zeolites for CF4 capture from electronics industry gas

The capture and separation of CF4 from CF4/N2 mixture gas is a crucial issue in the electronics industry, as CF4 is a commonly used etching gas and the ratio of CF4 to N2 directly affects process efficiency. Utilizing high-throughput computational screening techniques and grand canonical Monte Carlo (GCMC) simulations, we comprehensively screened and assessed 247 types of pure silicon zeolite materials to determine their adsorption and separation performance for CF4/N2 mixtures. Based on screening, the relationships between the structural parameters and adsorption and separation properties were meticulously investigated. Four indicators including adsorption selectivity, working capacity, adsorbent performance score (APS), and regenerability (R%) were used to evaluate the performance of adsorbents. Based on the evaluation, we selected the top three best-performing zeolite structures for vacuum swing adsorption (LEV, AWW and ESV) and pressure swing adsorption (AVL, ZON, and ERI) processes respectively. Also, we studied the preferable adsorption sites of CF4 and N2 in the selected zeolite structures through centroid density distributions at the molecule level. We expect the study may provide some valuable guidance for subsequent experimental investigations on adsorption and separation of CF4/N2.

Formation of Polyimide Membranes via Non-Solvent Induced Phase Separation: Insight from Molecular Dynamics Simulations

Molecular dynamics simulations were applied to investigate the formation of P84 polyimide membranes through the non-solvent induced phase separation (NIPS) process, considering two scenarios: one using a conventional organic solvent like n-methyl-2-pyrrolidone (NMP) and the other a greener alternative, γ-butyrolactone (GBL), with water serving as the non-solvent. Different compositions of polymer solutions were established along the binodal boundaries of the respective systems, derived from experimental cloud point data on the ternary phase diagram. The resulting polymer membranes were analyzed and compared in terms of their morphology. The wettability of their surfaces was notably affected by the polymer content in the initial casting solution and demonstrated a correlation with the Brunauer-Emmet-Teller (BET) specific surface area of the associated polymer nanostructures. The GBL solvent systems produced porous polymers qualitatively similar to those obtained with NMP, albeit with slightly narrower pore size distributions.

Popping and Locking: Balanced Rigidity and Porosity of Zeolitic Imidazolate Frameworks for High-Productivity Methane Purification

Zeolitic imidazolate frameworks (ZIFs) hold great promise in carbon capture, owing to their structural designability and functional porosity. However, intrinsic linker dynamics limit their pressure-swing adsorption application to biogas upgrading and methane purification. Recently, a functionality-locking strategy has shown feasibility in suppressing such dynamics. Still, a trade-off between structural rigidity and uptake capacity remains a key challenge for optimizing their high-pressure CO2/CH4 separation performance. Here, we report a sequential structural locking (SSL) strategy for enhancing the CO2 capture capacity and CH4 purification productivity in dynamic ZIFs (dynaZIFs). Specifically, we isolated multiple functionality-locked phases, ZIF-78-lt, -ht1, and -ht2, by activation at 50, 160, and 210 °C, respectively. We observed multiple-level locking through gas adsorption and powder X-ray diffraction. We uncovered an SSL mechanism dominated by linker-linker π-π interactions that transit to C-H···O hydrogen bonds with binding energies increasing from -0.64 to -2.77 and -5.72 kcal mol-1, respectively, as evidenced by single-crystal X-ray diffraction and density functional theory calculations. Among them, ZIF-78-ht1 exhibits the highest CO2 capture capacity (up to 18.6 mmol g-1) and CH4 purification productivity (up to 7.6 mmol g-1) at 298 K and 30 bar. These findings provide molecular and energetic insights into leveraging framework flexibility through the SSL mechanism to optimize porous materials' separation performance.

Study of CHF3/CH2F2 Adsorption Separation in TIFSIX-2-Cu-i

Hydrofluorocarbons (HFCs) have important applications in different industries; however, they are environmentally unfriendly due to their high global warming potential (GWP). Hence, reclamation of used hydrofluorocarbons via energy-efficient adsorption-based separation will greatly contribute to reducing their impact on the environment. In particular, the separation of azeotropic refrigerants remains challenging, such as typical mixtures of CH2F2 (HFC-23) and CHF3 (HFC-32), due to a lack of adsorptive mechanisms. Metal-organic frameworks (MOFs) can provide a promising solution for the separation of CHF3-CH2F2 mixtures. In this study, the adsorption mechanism of CHF3-CH2F2 mixtures in TIFSIX-2-Cu-i was revealed at the microscopic level by combining static pure-component adsorption experiments, molecular simulations, and density-functional theory (DFT) calculations. The adsorption separation selectivity of CH2F2/CHF3 in TIFSIX-2-Cu-i is 3.17 at 3 bar under 308 K. The existence of similar TiF62- binding sites for CH2F2 or CHF3 was revealed in TIFSIX-2-Cu-i. Interactions between the fluorine atom of the framework and the hydrogen atom of the guest molecule were found to be responsible for determining the high adsorption separation selectivity of CH2F2/CHF3. This exploration is important for the design of highly selective adsorbents for the separation of azeotropic refrigerants.

Exploring the Potential of Metal-Organic Frameworks for Cryogenic Helium-Based Gas Gap Heat Switches via High-Throughput Computational Screening

With the advantages of a long lifetime and high reliability, gas gap heat switches (GGHSs) are attractive in many thermal management applications, especially in space-borne cryogenic systems. The performance of a GGHS is significantly affected by the adsorption characteristics of the adsorbent in the sorption pump. Compared with the commonly used adsorbent in the GGHSs (activated carbon), metal-organic frameworks (MOFs) have larger surface areas, higher pore volumes, and exceptional tunability, which motivates this study to explore their potential for application in cryogenic GGHSs. To this end, two performance metrics, the required volume of adsorbent (vsor) and total input heat (qtot), were computed for about 6000 MOFs via molecular simulations and compared with those of activated carbon. It is found that over 2300 MOFs possess a smaller vsor than activated carbon, and the smallest vsor of MOFs is about 12.7% of that of activated carbon. vsor and qtot generally change in the same direction, which implies it is possible to reduce both parameters simultaneously by choosing a suitable MOF. Structure-performance analysis reveals that 1/vsor consistently increases first and then decreases with pore limiting diameter, largest cavity diameter, available pore volume, accessible surface area, helium void fraction, and bulk density. Descriptor ranges corresponding to high-performing MOFs were identified based on Precision-Recall analysis. Notably, Zr-containing MOFs are particularly likely to have smaller vsor values than activated carbon. It is anticipated that the promising MOFs identified by this study will motivate further experimental investigations, and the insights into structure-performance relationships can serve to guide the rational design of novel MOF candidates for GGHSs.

Tailoring ammonia capture in MOFs and COFs: A multi-scale and machine learning comprehensive investigation of functional group modification

Our study aims to examine the impact of ligand functionalization on the ammonia adsorption properties of MOFs and COFs, by combining multi-scale calculations with machine learning techniques. Density Functional Theory calculations were performed to investigate the interactions between ammonia (NH3) and a comprehensive set of 48 strategically chosen functional groups. In all of the cases, it is observed that functionalized rings exhibit a stronger interaction with ammonia molecule compared to unfunctionalized benzene, while -O2Mg demonstrates the highest interaction energy with ammonia (15 times stronger than the bare benzene). The trend obtained from the thorough DFT screening is verified via Grand Canonical Monte-Carlo calculations by employing interatomic potentials derived from quantum chemical calculations. Isosteric heat of adsorption plots provide a comprehensive elucidation of the adsorption process, and important insights can be taken for studies in fine-tuning materials for ammonia adsorption. Furthermore, a proof of concept machine learning (ML) analysis is conducted, which demonstrates that ML can accurately predict NH3 binding energies despite the limited amount of data. The findings derived from our multi-scale methodology indicate that the functionalization strategy can be utilized to guide synthesis towards MOFs, COFs, or other porous materials for enhanced NH3 adsorption capacity.

Constrained and Open Mesoporosity in Polypropylene Cracking: Insight From Spectroscopic Investigations of Acidity, Diffusion, and Activity

The outcome of the demetalation process of zeolites depends on applied treatment conditions and can lead to the formation of either open or constrained mesopores. The quaternary ammonium cations as pore-directing agents during desilication are responsible for developing constrained mesoporosity with bottleneck entrances. However, higher mesopore surface area and higher accessibility of acid sites are often found for the hierarchical zeolites with constrained mesopores. This is followed by better catalytic activity in the cracking of vacuum gas oil and polymers. For desilication with pure NaOH, a realumination process is observed and an additional acid-wash step is required to reach their full catalytic potential. Thus, this study aims to analyze the acidic and catalytic properties of hierarchical ZSM-5 zeolites of different mesoporosity types employing in situ and operando FT-IR spectroscopic evaluation of polypropylene cracking. The suitability of constrained mesoporosity is studied by assessing the neopentane diffusion in kinetic adsorption, Monte Carlo calculations, and rapid scan FT-IR spectroscopic measurement analyzed by Crank solution for diffusion. The FT-IR spectroscopic results of in situ and operando studies are supported by two-dimensional correlation analysis, allowing to establish the direction of changes seen on spectra and their order.

Faster Sorption of Propylene Compared to Propane Using an Elastic Layer-Structured Metal-Organic Framework (ELM-11)

The separation of propane and propylene is the most energy-consuming and difficult separation process in the petrochemical industry because of their extremely similar physical properties. Separating propylene from propane using sorption can considerably reduce the energy consumed by current cryogenic distillation techniques. However, sorption involves several major challenges. An elastic layer-structured metal-organic framework (ELM-11) exhibited a highly efficient propane/propylene sorption separation, owing to its kinetic properties. Under equilibrium conditions, propane and propylene exhibited similar sorption capacities, gate opening pressures, and heats of sorption. Thus, their separation under equilibrium conditions is impractical. However, the sorption rates of the two gases were considerably different, showing different diffusion coefficients, resulting in a high kinetic selectivity (214 at 298 K) of propylene over propane on ELM-11. This kinetic selectivity is considerably higher than those obtained in previous studies. Thus, ELM-11 is a promising sorbent for separation technologies.

Predicted Separation of Acid Gases from Gas Mixtures by Functionalized Porous Aromatic Frameworks

The selective adsorption of target acid gas molecules from binary gas mixtures by porous aromatic frameworks (PAFs) with two identical functional groups per aromatic ring (PAF-R2) was computationally investigated using grand canonical Monte Carlo simulations. PAF-R2 adsorption was considered for three binary mixtures of small molecular concentrations of acid gas and abundant nitrogen gas (CO2/N2, SO2/N2, and H2S/N2). The results indicate that additional functional groups enhance acid gas loadings and selectivity, compared with pristine PAF and single-functionalized PAFs. Low pressures yield linearly increasing gas loadings and constant selectivity, while high pressures yield much higher adsorption and selectivity. In particular, SO2 loading and selectivity under high pressures are heavily influenced by the PAF's maximum adsorption limit, which can be linked back to the functional groups and their configuration. In summary, PAF-(3,5)-(COOH)2 (nomenclature of PAFs is provided in the Appendix in the Supporting Information) and many other PAF with the same two electron-withdrawing groups are predicted to have great acid gas adsorption and selectivity from gas mixtures, while PAF-(3,5)-(OH)2 (one of PAFs with two identical electron-donating groups) is predicted to have good adsorption and selectivity, especially under elevated pressures. The results of this work can provide insights into various types of PAFs with great selective adsorption ability and their corresponding conditions. The simulation procedures and results may inspire the exploration and screening of other types of PAFs or porous materials, for acid gas absorption.

Computational screening and functional tuning of chemically stable metal organic frameworks for I2/CH3I capture in humid environments

High chemical stability is of vital significance in rendering metal organic frameworks (MOFs) as promising adsorbents for capturing leaked radioactive nuclides, under real nuclear industrial conditions with high humidity. In this work, grand canonical Monte Carlo (GCMC) and density functional theory (DFT) methods have been employed to systematically evaluate I2/CH3I capture performances of 21 experimentally confirmed chemically stable MOFs in humid environments. Favorable structural factors and the influence of hydrophilicity for iodine capture were unveiled. Subsequently, the top-performing MIL-53-Al with flexible tunability was functionalized with different functional groups to achieve the better adsorption performance. It has been revealed that the adsorption affinity and pore volume were two major factors altered by the functionalization of polar functional groups, which collectively influenced the iodine adsorption properties. In general, this work has screened the chemically stable high-performance MOF iodine adsorbents and provided comprehensive insights into the key factors affecting I2/CH3I uptake and separation in humid environments.

Investigation of the Dynamic Behaviour of H2 and D2 in a Kinetic Quantum Sieving System

Porous organic cages (POCs) are nanoporous materials composed of discrete molecular units that have uniformly distributed functional pores. The intrinsic porosity of these structures can be tuned accurately at the nanoscale by altering the size of the porous molecules, particularly to an optimal size of 3.6 Å, to harness the kinetic quantum sieving effect. Previous research on POCs for isotope separation has predominantly centered on differences in the quantities of adsorbed isotopes. However, nuclear quantum effects also contribute significantly to the dynamics of the sorption process, offering additional opportunities for separating H2 and D2 at practical operational temperatures. In this study, our investigations into H2 and D2 sorption on POC samples revealed a higher uptake of D2 compared to that of H2 under identical conditions. We employed quasi-elastic neutron scattering to study the diffusion processes of D2 and H2 in the POCs across various temperature and pressure ranges. Additionally, neutron Compton scattering was utilized to measure the values of the nuclear zero-point energy of individual isotopic species in D2 and H2. The results indicate that the diffusion coefficient of D2 is approximately one-sixth that of H2 in the POC due to the nuclear quantum effect. Furthermore, the results reveal that at 77 K, D2 has longer residence times compared to H2 when moving from pore to pore. Consequently, using the kinetic difference of H2 and D2 in a porous POC system enables hydrogen isotope separation using a temperature or pressure swing system at around liquid nitrogen temperatures.

Probing Structural Defects in MOFs Using Water Stability

Defects in the crystal structures of metal-organic frameworks (MOFs), whether present intrinsically or introduced via so-called defect engineering, can play strong roles in the properties of MOFs for various applications. Unfortunately, direct experimental detection and characterization of defects in MOFs are very challenging. We show that in many cases, the differences between experimentally observed and computationally predicted water stabilities of MOFs can be used to deduce information on the presence of point defects in real materials. Most computational studies of MOFs consider these materials to be defect-free, and in many cases, the resulting structures are predicted to be hydrophobic. Systematic experimental studies, however, have shown that many MOFs are hydrophilic. We show that the existence of chemically plausible point defects can often account for this discrepancy and use this observation in combination with detailed molecular simulations to assess the impact of local defects and flexibility in a variety of MOFs for which defects had not been considered previously.

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.

High-throughput computational screening of MOF adsorbents for efficient propane capture from air and natural gas mixtures

In this study, we used a high-throughput computational screening approach to examine the potential of metal-organic frameworks (MOFs) for capturing propane (C3H8) from different gas mixtures. We focused on Quantum MOF (QMOF) database composed of both synthesized and hypothetical MOFs and performed Grand Canonical Monte Carlo (GCMC) simulations to compute C3H8/N2/O2/Ar and C3H8/C2H6/CH4 mixture adsorption properties of MOFs. The separation of C3H8 from air mixture and the simultaneous separation of C3H8 and C2H6 from CH4 were studied for six different adsorption-based processes at various temperatures and pressures, including vacuum-swing adsorption (VSA), pressure-swing adsorption (PSA), vacuum-temperature swing adsorption (VTSA), and pressure-temperature swing adsorption (PTSA). The results of molecular simulations were used to evaluate the MOF adsorbents and the type of separation processes based on selectivity, working capacity, adsorbent performance score, and regenerability. Our results showed that VTSA is the most effective process since many MOFs offer high regenerability (>90%) combined with high C3H8 selectivity (>7 × 103) and high C2H6 + C3H8 selectivity (>100) for C3H8 capture from air and natural gas mixtures, respectively. Analysis of the top MOFs revealed that materials with narrow pores (<10 Å) and low porosities (<0.7), having aromatic ring linkers, alumina or zinc metal nodes, typically exhibit a superior C3H8 separation performance. The top MOFs were shown to outperform commercial zeolite, MFI for C3H8 capture from air, and several well-known MOFs for C3H8 capture from natural gas stream. These results will direct the experimental efforts to the most efficient C3H8 capture processes by providing key molecular insights into selecting the most useful adsorbents.

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.

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.

Alkoxy-Functionalized Hydroxamate/Zinc Metal-Organic Frameworks and the Effects of Substituents and Acid Addition on Their Structures

Single crystals of alkoxy-functionalized hydroxamate/zinc metal-organic frameworks (MOFs) were obtained by fixating the hydroxamate moiety via intramolecular hydrogen bonding. The resulting MOF structures depend on the steric demand of the alkoxy groups, whereby the incorporation of bulky isopropyl groups affords porous hydroxamate/zinc MOFs. The topological structures of the isopropyl-substituted MOFs could be controlled by adding acid.

Water adsorption on MoS2 under realistic atmosphere conditions and impacts on tribology

Molybdenum disulfide (MoS2) is a 2D material widely used as a dry lubricant. However, exposure to water and oxygen is known to reduce its effectiveness, and therefore an understanding of the uptake of water is important information for mitigating these effects. Here we use grand canonical Monte Carlo simulations to rigorously study water adsorption on MoS2 surfaces and edges with different concentrations of defects under realistic atmospheric conditions (i.e. various temperatures and humidity levels). We find that the amount of water adsorbed depends strongly on the number of defects. Simulations indicate that defect sites are generally saturated with water even at low ppm levels of humidity. Water binds strongly to S vacancies on interlamellar surfaces, but generally only one water molecule can fit on each of these sites. Defects on surfaces or edges of lamellae also strongly attract water molecules that then nucleate small clusters of water bonded via hydrogen bonding. We demonstrate that water preferentially binds to surface defects, but once those are saturated at a critical humidity level of about 500-1000 ppm water, water binds to edge sites where it negatively impacts the tribological performance of MoS2.

Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal-Organic Frameworks: Application to the Adsorptive Separation of C3 Components

The shape and topology of pores have significant impacts on the gas storage properties of nanoporous materials. Metal-organic frameworks (MOFs) are ideal materials with which to tailor to the needs of specific applications, due to properties such as their tunable structure and high specific surface area. It is, therefore, particularly important to develop descriptors that accurately identify the topological features of MOF pores. In this work, a topological data analysis method was used to develop a topological descriptor, based on the pore topology, which was combined with the Extreme Gradient Boosting (XGBoost) algorithm to predict the adsorption performance of MOFs for methane/ethane/propane. The final results show that this descriptor can accurately predict the performance of MOFs, and the introduction of the topological descriptor also significantly improves the accuracy of the model, resulting in an increase of up to 17.55% in the R2 value of the model and a decrease of up to 46.1% in the RMSE, compared to commonly used models that are based on the structural descriptor. The results of this study contribute to a deeper understanding of the relationship between the performance and structure of MOFs and provide useful guidelines and strategies for the design of high-performance separation materials.