Unlocking the Effect of H2O on CO2 Separation Performance of Promising MOFs Using Atomically Detailed Simulations

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.

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.

Efficient Generation of Large Collections of Metal-Organic Framework Structures Containing Well-Defined Point Defects

High-throughput molecular simulations of metal-organic frameworks (MOFs) are a useful complement to experiments to identify candidates for chemical separation and storage. All previous efforts of this kind have used simulations in which MOFs are approximated as defect-free. We introduce a tool to readily generate missing-linker defects in MOFs and demonstrate this tool with a collection of 507 defective MOFs. We introduce the concept of the maximum possible defect concentration; at higher defect concentrations, deviations from the defect-free crystal structure would be readily evident experimentally. We studied the impact of defects on molecular adsorption as a function of defect concentrations. Defects have a slightly negative or negligible influence on adsorption at low pressures for ethene, ethane, and CO₂ but a strong positive influence for methanol due to hydrogen bonding with defects. Defective structures tend to have loadings slightly higher than those of defect-free structures for all adsorbates at elevated pressures.

High-Throughput Screening of the CoRE-MOF-2019 Database for CO2 Capture from Wet Flue Gas: A Multi-Scale Modeling Strategy

Stabilizing the escalating CO₂ levels in the atmosphere is a grand challenge in view of the increasing global demand for energy, the majority of which currently comes from the burning of fossil fuels. Capturing CO₂ from point source emissions using solid adsorbents may play a part in meeting this challenge, and metal-organic frameworks (MOFs) are considered to be a promising class of materials for this purpose. It is important to consider the co-adsorption of water when designing materials for CO₂ capture from post-combustion flue gases. Computational high-throughput screening (HTS) is a powerful tool to identify top-performing candidates for a particular application from a large material database. Using a multi-scale modeling strategy that includes a machine learning model, density functional theory (DFT) calculations, force field (FF) optimization, and grand canonical Monte Carlo (GCMC) simulations, we carried out a systematic computational HTS of the all-solvent-removed version of the computation-ready experimental metal-organic framework (CoRE-MOF-2019) database for selective adsorption of CO₂ from a wet flue gas mixture. After initial screening based on the pore diameters, a total of 3703 unique MOFs from the database were considered for screening based on the FF interaction energies of CO₂, N₂, and H₂O molecules with the MOFs. MOFs showing stronger interactions with CO₂ compared to that with H₂O and N₂ were considered for the next level of screening based on the interaction energies calculated from DFT. CO₂-selective MOFs from DFT screening were further screened using two-component (CO₂ and N₂) and finally three-component (CO₂, N₂, and H₂O) GCMC simulations to predict the CO₂ capacity and CO₂/N₂ selectivity. Our screening study identified MOFs that show selective CO₂ adsorption under wet flue gas conditions with significant CO₂ uptake capacity and CO₂/N₂ selectivity in the presence of water vapor. We also analyzed the nature of pore confinements responsible for the observed CO₂ selectivity.

Water Bridges Substitute for Defects in Amine-Functionalized UiO-66, Boosting CO2 Adsorption

The binary adsorption of CO₂ and water on an amine-functionalized UiO-66 metal-organic framework (MOF) was studied experimentally and computationally. Grand canonical Monte Carlo simulations were used to investigate three additional UiO-66 MOFs with different functionalized linkers. Each MOF was studied in a defect-free form as well as two additional forms with precise linker defects. Binary adsorption isotherms are presented for CO₂ at specific water loadings. While water loading in defect-free MOFs reduces the CO₂ uptake, the defects slightly boost the CO₂ uptake at low water loadings. It was found that water bridges form between the metal oxide cores, replacing the missing linkers. Effectively, this creates smaller pores that are more welcoming of CO₂ adsorption. Experimental measurement of the binary isotherms for UiO-66-NH₂ shows a behavior that is consistent with this enhancement.

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.