Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF4]/MOF Composites for CO2/N2 Separation.

Considering the existence of a large number and variety of metal-organic frameworks (MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF composites by purely experimental methods is not practical. In this work, we combined molecular simulations and machine learning (ML) algorithms to computationally design an IL/MOF composite. Molecular simulations were first performed to screen approximately 1000 different composites of 1-n-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]) with a large variety of MOFs for CO2 and N2 adsorption. The results of simulations were used to develop ML models that can accurately predict the adsorption and separation performances of [BMIM][BF4]/MOF composites. The most important features that affect the CO2/N2 selectivity of composites were extracted from ML and utilized to computationally generate an IL/MOF composite, [BMIM][BF4]/UiO-66, which was not present in the original material data set. This composite was finally synthesized, characterized, and tested for CO2/N2 separation. Experimentally measured CO2/N2 selectivity of the [BMIM][BF4]/UiO-66 composite matched well with the selectivity predicted by the ML model, and it was found to be comparable, if not higher than that of all previously synthesized [BMIM][BF4]/MOF composites reported in the literature. Our proposed approach of combining molecular simulations with ML models will be highly useful to accurately predict the CO2/N2 separation performances of any [BMIM][BF4]/MOF composite within seconds compared to the extensive time and effort requirements of purely experimental methods.

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

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

Metal-organic framework-based materials for the abatement of air pollution and decontamination of wastewater.

Developing new and efficient technologies for environmental remediation is becoming significant due to the increase in global concerns such as climate change, severe epidemics, and energy crises. Air pollution, primarily due to increased levels of H2S, SOx, NH3, NOx, CO, volatile organic compounds (VOC), and particulate matter (PM) in the atmosphere, has a significant impact on public health, and exhaust gases harm the natural sulfur, nitrogen, and carbon cycles. Similarly, wastewater discharged to the environment with metal ions, herbicides, pharmaceuticals, personal care products, dyes, and aromatics/organic compounds is a risk for health since it may lead to an outbreak of waterborne pathogens and increase the exposure to endocrine-disrupting agents. Therefore, developing new and efficient air and water quality management systems is critical. Metal-organic frameworks (MOFs) are novel materials for which the main application areas include gas storage and separation, water harvesting from the atmosphere, chemical sensing, power storage, drug delivery, and food preservation. Due to their versatile structural motifs that can be modified during synthesis, MOFs also have a great promise for green applications including air and water pollution remediation. The motivation to use MOFs for environmental applications prompted the modification of their structures via the addition of metal and functional groups, as well as the creation of heterostructures by mixing MOFs with other nanomaterials, to effectively remove hazardous contaminants from wastewater and the atmosphere. In this review, we focus on the state-of-the-art environmental applications of MOFs, particularly for water treatment and air pollution, by highlighting the groundbreaking studies in which MOFs have been used as adsorbents, membranes, and photocatalysts for the abatement of air and water pollution. We finally address the opportunities and challenges for the environmental applications of MOFs.

Prediction of O2/N2 Selectivity in Metal-Organic Frameworks via High-Throughput Computational Screening and Machine Learning.

Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this study, the computation-ready experimental (CoRE) metal-organic framework (MOF) data set for which the O2 and N2 uptakes, self-diffusivities, and Henry's constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O2/N2 selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with r2 correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure-property relationship and to identity the top performing hMOFs for O2/N2 separation based on the adsorption and diffusion selectivity.

Recent advances in simulating gas permeation through MOF membranes.

In the last two decades, metal organic frameworks (MOFs) have gained increasing attention in membrane-based gas separations due to their tunable structural properties. Computational methods play a critical role in providing molecular-level information about the membrane properties and identifying the most promising MOF membranes for various gas separations. In this review, we discuss the current state-of-the-art in molecular modeling methods to simulate gas permeation through MOF membranes and review the recent advancements. We finally address current opportunities and challenges of simulating gas permeation through MOF membranes to guide the development of high-performance MOF membranes in the future.

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

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

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.

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.

Computer simulations of 4240 MOF membranes for H2/CH4 separations: insights into structure-performance relations.

Design of new membranes having high H2/CH4 selectivity and high H2 permeability is strongly desired to reduce the energy demand for H2 production. Metal organic frameworks (MOFs) offer a great promise for membrane-based gas separations due to their tunable physical and chemical properties. We performed a high-throughput computational screening study to examine membrane-based H2/CH4 separation potentials of 4240 MOFs. Grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations were used to compute adsorption and diffusion of H2 and CH4 in MOFs. Simulation results were then used to predict adsorption selectivity, diffusion selectivity, gas permeability and membrane selectivity of MOFs. A large number of MOF membranes was found to outperform traditional polymer and zeolite membranes by exceeding the Robeson's upper bound for selective separation of H2 from CH4. Structure-performance analysis was carried out to understand the relations between MOF membranes' selectivities and their pore sizes, surface areas, porosities, densities, lattice systems, and metal types. Results showed that MOFs with pore limiting diameters between 3.8 and 6 Å, the largest cavity diameters between 6 and 12 Å, surface areas less than 1000 m2 g-1, porosities between 0.5 and 0.75, and densities between 1 and 1.5 g cm-3 are the most promising membranes leading to H2 selectivities >10 and H2 permeabilities >104 Barrer. Our results suggest that monoclinic MOFs having copper metals are the best membrane candidates for H2/CH4 separations. This study represents the first high-throughput computational screening of the most recent MOF database for membrane-based H2/CH4 separation and microscopic insight provided from molecular simulations will be highly useful for the future design of new MOFs having extraordinarily high H2 selectivities.

Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening.

Metal-organic frameworks (MOFs) are potential adsorbents for CO2 capture. Because thousands of MOFs exist, computational studies become very useful in identifying the top performing materials for target applications in a time-effective manner. In this study, molecular simulations were performed to screen the MOF database to identify the best materials for CO2 separation from flue gas (CO2/N2) and landfill gas (CO2/CH4) under realistic operating conditions. We validated the accuracy of our computational approach by comparing the simulation results for the CO2 uptakes, CO2/N2 and CO2/CH4 selectivities of various types of MOFs with the available experimental data. Binary CO2/N2 and CO2/CH4 mixture adsorption data were then calculated for the entire MOF database. These data were then used to predict selectivity, working capacity, regenerability, and separation potential of MOFs. The top performing MOF adsorbents that can separate CO2/N2 and CO2/CH4 with high performance were identified. Molecular simulations for the adsorption of a ternary CO2/N2/CH4 mixture were performed for these top materials to provide a more realistic performance assessment of MOF adsorbents. The structure-performance analysis showed that MOFs with Δ Qst0 > 30 kJ/mol, 3.8 Å < pore-limiting diameter < 5 Å, 5 Å < largest cavity diameter < 7.5 Å, 0.5 < ϕ < 0.75, surface area < 1000 m2/g, and ρ > 1 g/cm3 are the best candidates for selective separation of CO2 from flue gas and landfill gas. This information will be very useful to design novel MOFs exhibiting high CO2 separation potentials. Finally, an online, freely accessible database https://cosmoserc.ku.edu.tr was established, for the first time in the literature, which reports all of the computed adsorbent metrics of 3816 MOFs for CO2/N2, CO2/CH4, and CO2/N2/CH4 separations in addition to various structural properties of MOFs.