Effects of Force Field Selection on the Computational Ranking of MOFs for CO2 Separations

Computational and Machine Learning Methods for CO2 Capture Using Metal-Organic Frameworks

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made significant progress and provided benefits in the fields of chemistry and material science. This work examines the interactions between chemistry and materials computational science at the atomic and molecular scales for metal-organic framework (MOF) adsorbent development toward carbon dioxide (CO2) capture. Herein, a connection will be drawn between atomic forces predicted by ML algorithms and the structures of MOFs for CO2 adsorption. Our study also takes into account the successes of atomic computational screening in the field of materials science, especially quantum ML, and its relationship to ML algorithms that clarify advancements in the area of CO2 adsorption by MOFs. Additionally, we reviewed the processes for supplying data to ML algorithms for algorithm training, including text mining from scientific articles, and MOF's formula processing linked to the chemical properties of MOFs. To create ML algorithms for future research, we recommend that the digitization of scientific records can help efficiently synthesize advanced MOFs. Finally, a future vision for developing pioneer MOF synthesis routes for CO2 capture is presented in this review article.

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.

Nanofibrous membranes comprising intrinsically microporous polyimides with embedded metal-organic frameworks for capturing volatile organic compounds

Here, we report the fabrication of nanofibrous air-filtration membranes of intrinsically microporous polyimide with metal-organic frameworks (MOFs). The membranes successfully captured VOCs from air. Two polyimides with surface areas up to 500 m² g^-1 were synthesized, and the impact of the porosity on the sorption kinetics and capacity of the nanofibers were investigated. Two Zr-based MOFs, namely pristine UiO-66 (1071 m² g^-1) and defective UiO-66 (1582 m² g^-1), were embedded into the nanofibers to produce nanocomposite materials. The nanofibers could remove polar formaldehyde and non-polar toluene, xylene, and mesitylene from air. The highest sorption capacity with 214 mg g^-1 was observed for xylene, followed by mesitylene (201 mg g^-1), toluene (142 mg g^-1), and formaldehyde (124 mg g^-1). The incorporation of MOFs drastically improved the sorption performance of the fibers produced from low-surface-area polyimide. Time-dependent sorption tests revealed the rapid sequestration of air pollutants owing to the intrinsic porosity of the polyimides and the MOF fillers. The porosity allowed the rapid diffusion of pollutants into the inner fiber matrix. The molecular level interactions between VOCs and polymer/MOFs were clarified by molecular modeling studies. The practicality of material fabrication and the applicability of the material were assessed through the modification of industrial N95 dust masks. To the best of our knowledge, this is the first successful demonstration of the synergistic combination of intrinsically microporous polyimides and MOFs in the form of electrospun nanofibrous membranes and their application for VOC removal.

Molecular dynamics simulation for membrane separation and porous materials: A current state of art review

Computational frameworks have been under specific attention within the last two decades. Molecular Dynamics (MD) simulations, identical to the other computational approaches, try to address the unknown question, lighten the dark areas of unanswered questions, to achieve probable explanations and solutions. Owing to their complex microporous structure on one side and the intricate biochemical nature of various materials used in the structure, separative membrane materials possess peculiar degrees of complications. More notably, as nanocomposite materials are often integrated into separative membranes, thin-film nanocomposites and porous separative nanocomposite materials could possess an additional level of complexity with regard to the nanoscale interactions brought to the structure. This critical review intends to cover the recent methods used to assess membranes and membrane materials. Incorporation of MD in membrane technology-related fields such as desalination, fuel cell-based energy production, blood purification through hemodialysis, etc., were briefly covered. Accordingly, this review could be used to understand the current extent of MD applications for separative membranes. The review could also be used as a guideline to use the proper MD implementation within the related fields.

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

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

Molecular simulations of the adsorption and separation of hydrogen sulfide, carbon dioxide, methane, and nitrogen and their binary mixtures (H2S/CH4), (CO2/CH4) on NUM-3a metal-organic frameworks

In this work, the adsorptions of carbon dioxide, methane, nitrogen, and hydrogen sulfide and the separation of their binary mixtures into NUM-3a Metal-Organic Framework (MOF) were studied through Grand Canonical Monte Carlo (GCMC) simulation method. The simulated pure gas uptakes using three generic force fields (UFF, Dreiding, and OPLS) at 298 K were compared with the experimental values. The accuracy of the applied force fields for each gas was compared with the experimental isotherms and discussed. Our results show that OPLS has the best accuracy in the case of methane while Dreiding was the best for CO₂ and N₂. Simulated gas uptakes indicated that H₂S was more adsorbed by NUM-3a than CO₂, CH₄, and N₂. The calculated adsorption selectivity of NUM-3a for the binary mixtures of CH₄ with H₂S is larger than that of CO₂. NUM-3a possess more affinity for H₂S and CO₂ than for CH₄, where it may be a promising adsorbent material for separating carbon dioxide and hydrogen sulfide from methane. Furthermore, the most probable sites for the adsorption of the studied gases on the NUM-3a were investigated. The heats of adsorptions, as well as Henry's law constants, were also calculated, and it was in line with the observed gas adsorptions. The most preferred sites for the adsorption of carbon dioxide and hydrogen sulfide are the carboxyl groups and inside the channels and around the metal centers. However, methane and nitrogen are mainly accumulating in the channels' s apexes of NUM-3a around the metal center.

Computational Selection of High-Performing Covalent Organic Frameworks for Adsorption and Membrane-Based CO2/H2 Separation

Covalent organic frameworks (COFs) have high potential in gas separation technologies because of their porous structures, large surface areas, and good stabilities. The number of synthesized COFs already reached several hundreds, but only a handful of materials were tested as adsorbents and/or membranes. We used a high-throughput computational screening approach to uncover adsorption-based and membrane-based CO₂/H₂ separation potentials of 288 COFs, representing the highest number of experimentally synthesized COFs studied to date for precombustion CO₂ capture. Grand canonical Monte Carlo (GCMC) simulations were performed to assess CO₂/H₂ mixture separation performances of COFs for five different cyclic adsorption processes: pressure swing adsorption, vacuum swing adsorption, temperature swing adsorption (TSA), pressure-temperature swing adsorption (PTSA), and vacuum-temperature swing adsorption (VTSA). The results showed that many COFs outperform traditional zeolites in terms of CO₂ selectivities and working capacities and PTSA is the best process leading to the highest adsorbent performance scores. Combining GCMC and molecular dynamics (MD) simulations, CO₂ and H₂ permeabilities and selectivities of COF membranes were calculated. The majority of COF membranes surpass Robeson's upper bound because of their higher H₂ permeabilities compared to polymers, indicating that the usage of COFs has enormous potential to replace current materials in membrane-based H₂/CO₂ separation processes. Performance analysis based on the structural properties showed that COFs with narrow pores [the largest cavity diameter (LCD) < 15 Å] and low porosities (ϕ < 0.75) are the top adsorbents for selective separation of CO₂ from H₂, whereas materials with large pores (LCD > 20 Å) and high porosities (ϕ > 0.85) are generally the best COF membranes for selective separation of H₂ from CO₂. These results will help to speed up the engineering of new COFs with desired structural properties to achieve high-performance CO₂/H₂ separations.

Do New MOFs Perform Better for CO2 Capture and H2 Purification? Computational Screening of the Updated MOF Database

High-throughput computational screening of metal organic frameworks (MOFs) enables the discovery of new promising materials for CO₂ capture and H₂ purification. The number of synthesized MOFs is increasing very rapidly, and computation-ready, experimental MOF databases are being updated. Screening the most recent MOF database is essential to identify the best performing materials among several thousands. In this work, we performed molecular simulations of the most recent MOF database and described both the adsorbent and membrane-based separation performances of 10 221 MOFs for CO₂ capture and H₂ purification. The best materials identified for pressure swing adsorption, vacuum swing adsorption, and temperature swing adsorption processes outperformed commercial zeolites and previously studied MOFs in terms of CO₂ selectivity and adsorbent performance score. We then discussed the applicability of Ideal Adsorbed Solution Theory (IAST), effects of inaccessible local pores and catenation in the frameworks and the presence of impurities in CO₂/H₂ mixture on the adsorbent performance metrics of MOFs. Very large numbers of MOF membranes were found to outperform traditional polymer and porous membranes in terms of H₂ permeability. Our results show that MOFs that are recently added into the updated MOF database have higher CO₂/H₂ separation potentials than the previously reported MOFs. MOFs with small pores were identified as potential adsorbents for selective capture of CO₂ from H₂, whereas MOFs with high porosities were the promising membranes for selective separation of H₂ from CO₂. This study reveals the importance of enriching the number of MOFs in high-throughput computational screening studies for the discovery of new promising materials for CO₂/H₂ separation.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time-consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network-specifically, a multilayer perceptron (MLP)-as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our MLP on "alchemical" species, represented only by variables derived from their force-field parameters, to predict the loadings of real adsorbates. Alchemical species used for training were small, near-spherical, and nonpolar, enabling the prediction of analogous real molecules relevant for chemical separations such as argon, krypton, xenon, methane, ethane, and nitrogen. MOFs were also represented by simple descriptors (e.g., geometric properties and chemical moieties). The trained model was shown to make accurate adsorption predictions for these six adsorbates in both hypothetical and existing MOFs. The MLP presented here is not expected to be applied "as is" to more complex adsorbates with properties not considered during its training. However, our results illustrate a new philosophy of training that can be built upon with the goal of predicting adsorption isotherms in not only a database of MOFs but also a database of adsorbates and over a range of relevant operating conditions.

The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation

Metal-organic frameworks (MOFs) are highly tuneable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinise the computational discovery of MOFs for adsorption-based engineering applications.

Computational Screening of Metal-Organic Frameworks for Membrane-Based CO2/N2/H2O Separations: Best Materials for Flue Gas Separation

It has become a significant challenge to select the best metal-organic frameworks (MOFs) for membrane-based gas separations because the number of synthesized MOFs is growing exceptionally fast. In this work, we used high-throughput computational screening to identify the top MOF membranes for flue gas separation. Grand canonical Monte Carlo and molecular dynamics simulations were performed to assess adsorption and diffusion properties of CO₂ and N₂ in 3806 different MOFs. Using these data, selectivities and permeabilities of MOF membranes were predicted and compared with those of conventional membranes, polymers, and zeolites. The best performing MOF membranes offering CO₂/N₂ selectivity > 350 and CO₂ permeability > 10⁶ Barrer were identified. Ternary CO₂/N₂/H₂O mixture simulations were then performed for the top MOFs to unlock their potential under industrial operating conditions, and results showed that the presence of water decreases CO₂/N₂ selectivity and CO₂ permeability of some MOF membranes. As a result of this stepwise screening procedure, the number of promising MOF membranes to be investigated for flue gas separation in future experimental studies was narrowed down from thousands to tens. We finally examined the structure-performance relations of MOFs to understand which properties lead to the greatest promise for flue gas separation and concluded that lanthanide-based MOFs with narrow pore openings (<4.5 Å), low porosities (<0.75), and low surface areas (<1000 m²/g) are the best materials for membrane-based CO₂/N₂ separations.