Predicting of Covalent Organic Frameworks for Membrane-based Isobutene/1,3-Butadiene Separation: Combining Molecular Simulation and Machine Learning

Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking organic ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO2 capture, CH4 storage, gas separation, catalysis, etc. Traditional methods of material research, which mainly rely on manual experimentation, are not particularly efficient, while with advancements in computer science, high-throughput computational screening methods based on molecular simulation have become crucial in material discovery, yet they face limitations in terms of computational resources and time. Currently, machine learning (ML) has emerged as a transformative tool in many fields, capable of analyzing large data sets, identifying underlying patterns, and predicting material performance efficiently and accurately. This approach, termed "materials genomics", combines high-throughput computational screening with ML to predict and design high-performance materials, significantly speeding up the discovery process compared to traditional methods. This review discusses the functions of ML in the screening, design, and performance prediction of COFs and highlights their applications across various domains like CO2 capture, CH4 storage, gas separation, and catalysis, thereby providing new research directions and enhancing the understanding of COF materials and their applications.

Accelerated Screening of Covalent Organic Frameworks for Ethylene Glycol/1,2-Butanediol Separation by Interpretable Machine Learning

The separation of ethylene glycol (EG) and 1,2-butanediol (1,2-BDO) azeotrope in the synthesis process of EG via coal and biomass is becoming increasingly commercial and of environmental importance. Selective adsorption is deemed as the most promising method because of energy savings and environment favorability. In this study, we developed an interpretable decision tree (DT) model to facilitate high-throughput screening of covalent organic frameworks (COFs) as adsorbents for the separation of EG/1,2-BDO mixtures, achieving an R2 value of 0.96. The interpretable decision tree analysis has shown that using the difference in isosteric heat (ΔQst) between EG (Qst-EG) and 1,2-BDO (Qst-BDO), combined with the largest cavity diameter (LCD), is effective for selecting the optimal COFs for EG/1,2-BDO separation. COFs with ΔQst greater than 6.5 kcal/mol and an LCD ranging from 3.6 to 4.8 Å typically exhibit superior performance and can serve as preselection criteria to accelerate the screening process. Six COFs with high EG working capacity and exceptional adsorption selectivity for EG/1,2-BDO were selected using the selection principle. All the selected COFs containing strong electronegative groups. The electronegative groups can significantly amplify the disparity in adsorption strength between EG and 1,2-BDO, thereby boosting separation efficiency. The principles proposed in this work can be used to guide the design of COFs for effective separation of EG and 1,2-BDO.

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.

Covalent organic frameworks for CO2 capture: from laboratory curiosity to industry implementation

CO2 concentration in the atmosphere has increased by about 40% since the 1960s. Among various technologies available for carbon capture, adsorption and membrane processes have been receiving tremendous attention due to their potential to capture CO2 at low costs. The kernel for such processes is the sorbent and membrane materials, and tremendous progress has been made in designing and fabricating novel porous materials for carbon capture. Covalent organic frameworks (COFs), a class of porous crystalline materials, are promising sorbents for CO2 capture due to their high surface area, low density, controllable pore size and structure, and preferable stabilities. However, the absence of synergistic developments between materials and engineering processes hinders achieving the qualitative leap for net-zero emissions. Considering the lack of a timely review on the combination of state-of-the-art COFs and engineering processes, in this Tutorial Review, we emphasize the developments of COFs for meeting the challenges of carbon capture and disclose the strategies of fabricating COFs for realizing industrial implementation. Moreover, this review presents a detailed and basic description of the engineering processes and industrial status of carbon capture. It highlights the importance of machine learning in integrating simulations of molecular and engineering levels. We aim to stimulate both academia and industry communities for joined efforts in bringing COFs to practical carbon capture.

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

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

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

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

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.