Machine Learning-Enabled Framework for High-Throughput Screening of MOFs: Application in Radon/Indoor Air Separation

Ren, Junyu; Wang, Shihui; Bi, Kexin; Cheng, Min; Liu, Chong; Zhou, Li; Xue, Xiaoyu; Ji, Xu

doi:10.1021/acsami.2c19207

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…It should be noted that in the literature, many studies considered similar lists of features to those that are used in this study. ,− However, the feature list could involve different features such as atom density, energy histograms, atomic property weighted radial distribution functions (AP-RDFs), revised autocorrelation functions (RACs), etc., which are being increasingly used. Both the features involved and not involved in this study can be used not only for MOFs but also for other porous materials such as covalent–organic frameworks (COFs) and zeolites. − It should be noted that, in contrast to this work, some of the studies in the literature involve ML models with more than two features to predict gas adsorption/separation performances. , In those ML models, many different features such as topology, void fraction, functional group density, most positive charge, most negative charge, metal angle, and surface atom density were utilized.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…However, on the basis of a qualitative trend obtained as shown in Figure , we performed a proof-of-concept study that showcases only 2000 GCMC steps are sufficient for high-throughput screening; however, a greater number of GCMC steps should be used to validate the uptake capacity of that MOF. Indeed, there are previous studies in the literature that have used ∼3,000–10,000 steps to perform high-throughput screening of MOFs for gas adsorption. − …”

Section: Resultsmentioning

confidence: 99%

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

Singh,

Sose,

Wang

et al. 2023

J. Chem. Theory Comput.

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Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal–organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.

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Weaving the gates of life: Pioneering a new era in oral gene delivery with metal-organic frameworks

Jiang,

Zhang,

Zeng

et al. 2025

Chemical Engineering Journal

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Machine Learning-Enabled Framework for High-Throughput Screening of MOFs: Application in Radon/Indoor Air Separation

Cited by 3 publications

References 53 publications

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

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

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

Weaving the gates of life: Pioneering a new era in oral gene delivery with metal-organic frameworks

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Machine Learning-Enabled Framework for High-Throughput Screening of MOFs: Application in Radon/Indoor Air Separation

Cited by 3 publications

References 53 publications

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

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

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

Weaving the gates of life: Pioneering a new era in oral gene delivery with metal-organic frameworks

Contact Info

Product

Resources

About

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

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