Evaluating CH<sub>4</sub>/N<sub>2</sub> Separation Performances of Hundreds of Thousands of Real and Hypothetical MOFs by Harnessing Molecular Modeling and Machine Learning

Gulbalkan, Hasan Can; Uzun, Alper; Keskin, Seda

doi:10.1021/acsami.3c13533

Cited by 4 publications

(2 citation statements)

References 68 publications

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“…In the pursuit of quickly and accurately identifying the most promising MOF adsorbents for CH 4 /N 2 separation, our group recently performed molecular simulations for 4612 synthesized MOFs and subsequently developed ML models to predict CH 4 /N 2 mixture adsorption data based on the pore size, surface area, pore volume, porosity, element percentages, and simulated Henry's constants of gases in MOFs. 45 As a result, adsorptionbased CH 4 /N 2 separation performance limits of a very large material spectrum, 4612 synthesized MOFs and 98 601 hMOFs, were revealed for the first time, and the existence of many promising MOF adsorbents having superior separation performance compared to zeolites and activated carbons was shown.…”

Section: Mof Adsorbentsmentioning

confidence: 97%

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Gulbalkan,

Uzun,

Keskin

2024

Applied Physics Letters

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Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have great potential to be used as porous adsorbents and membranes to achieve high-performance methane purification. Although the continuous increase in the number and diversity of MOFs and COFs is a great opportunity for the discovery of novel adsorbents and membranes with superior performances, evaluating such a vast number of materials in the quickest and most effective manner requires the development of computational approaches. High-throughput computational screening based on molecular simulations has been extensively used to identify the most promising MOFs and COFs for methane purification. However, the enormous and ever-growing material space necessitates more efficient approaches in terms of time and effort. Combining data science with molecular simulations has recently accelerated the discovery of optimal MOF and COF materials for methane purification and revealed the hidden structure–performance relationships. In this perspective, we highlighted the recent developments in combining high-throughput molecular simulations and machine learning to accurately identify the most promising MOF and COF adsorbents and membranes among thousands of candidates for separating methane from other gases including acetylene, carbon dioxide, helium, hydrogen, and nitrogen. After providing a brief overview of the topic, we reviewed the pioneering contributions in the field and discussed the current opportunities and challenges that we need to direct our efforts for the design and discovery of adsorbent and membrane materials.

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Section: Mof Adsorbentsmentioning

confidence: 97%

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Gulbalkan,

Uzun,

Keskin

2024

Applied Physics Letters

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“…By using machine learning (ML) methods, predictive models can be generated from a (sub)set of MOFs, which can be used to quickly acquire target data (e.g., adsorption amount) in similar MOFs. Successful examples of generating accurate ML methods for the prediction of gas adsorption properties of MOFs have been showcased in the literature. − While these examples highlight the benefits of ML models for various gas uptake predictions, the number of studies in the literature related to the separation of CF 4 is very limited. , Hu et al developed ML models for the separation of CF 4 /N 2 in thousands of MOFs using molecular simulation data. It was revealed that the importance of the model features can change drastically depending on the pressure.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF₄ from CH₄ and N₂

Demir,

Erucar

2024

Ind. Eng. Chem. Res.

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One of the most crucial problems of the modern world is to combat the emission of greenhouse gases as these lead to extreme weather conditions, droughts, etc., and endanger the global ecosystem. Among the various greenhouse gases, the removal of carbon tetrafluoride, CF 4 , from the atmosphere is of particular interest as its global warming potential is several orders of magnitude higher than that of many other greenhouse gases, including carbon dioxide, CO 2 . The separation of CF 4 from methane, CH 4 , and nitrogen, N 2 , which are present in the atmosphere alongside CF 4 , can be efficiently accomplished by adsorption-or membrane-based processes. Metal−organic frameworks (MOFs) offer a variety of possibilities for gas separation due to their large surface areas and tailored chemical and physical structures. In this work, the separation of CF 4 /CH 4 , CF 4 /N 2 , and CF 4 / CH 4 /N 2 in a subset of MOFs, double-linker MOFs (DL-MOFs), is investigated by using molecular simulations and machine learning (ML) methods. Among ∼500 MOFs, the best MOFs for adsorption-based CF 4 /CH 4 , CF 4 /N 2 , and CF 4 /CH 4 /N 2 separation are determined by considering the adsorption selectivity, working capacity, and regenerability. The ML models developed for adsorption-based CF 4 /CH 4 , CF 4 /N 2 , and CF 4 /CH 4 /N 2 separation have revealed that only one or two features (Henry's constant and/or surface area) are sufficient to establish models that can make accurate predictions of gas uptake. The membrane-based CF 4 /CH 4 and CF 4 /N 2 separation performances of DL-MOFs have also been studied, and it was found that inverse selective MOFs preferring CH 4 or N 2 over CF 4 can exist. Overall, this study will open avenues for the design and development of novel MOF adsorbents and membranes for the separation of CF 4 from CH 4 and N 2 by providing atomistic insights into the adsorption and diffusion properties of DL-MOFs.

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Advanced luminescent metal–organic framework (MOF) sensors engineered for urine analysis applications

Mohan,

Virender,

Kumar Gupta

et al. 2024

Coordination Chemistry Reviews

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Evaluating CH₄/N₂ Separation Performances of Hundreds of Thousands of Real and Hypothetical MOFs by Harnessing Molecular Modeling and Machine Learning

Cited by 4 publications

References 68 publications

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF₄ from CH₄ and N₂

Advanced luminescent metal–organic framework (MOF) sensors engineered for urine analysis applications

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Evaluating CH4/N2 Separation Performances of Hundreds of Thousands of Real and Hypothetical MOFs by Harnessing Molecular Modeling and Machine Learning

Cited by 4 publications

References 68 publications

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF4 from CH4 and N2

Advanced luminescent metal–organic framework (MOF) sensors engineered for urine analysis applications

Contact Info

Product

Resources

About

Evaluating CH₄/N₂ Separation Performances of Hundreds of Thousands of Real and Hypothetical MOFs by Harnessing Molecular Modeling and Machine Learning

Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF₄ from CH₄ and N₂