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Different applications of machine learning approaches in materials science and engineering: Comprehensive review

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Energy Reviews, 2025, 4, 100106
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High-throughput screening of covalent organic framework membranes separation for hydrogen from hydrogen-blended natural gas

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Applied Thermal Engineering, 2025, 271, 126333
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Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

Hao Wang, Yuquan Li, Xiaoyang Xuan, Kai Wang, Ye-feng Yao and Likun Pan

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

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Applied Physics Letters, 2024, 124
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Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics

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ACS Appl. Mater. Interfaces, 2024, 16, 19806
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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

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ACS Appl. Mater. Interfaces, 2025, 17, 17691
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Interpretable machine learning for materials discovery: Predicting CO2 adsorption properties of metal–organic frameworks

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APL Materials, 2024, 12
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The COF Space: Materials Features, Gas Adsorption, and Separation Performances Assessed by Machine Learning

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Two‐Dimensional Covalent Organic Frameworks in Organic Electronics

Qi Liu, Qiang Li, Yu Li, Taotao Su, Binghan Hou, Yibo Zhao and Youzhi Xu

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Two‐Dimensional Covalent Organic Frameworks in Organic Electronics

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Molecular simulations: From fundamental principles to applications in gaseous pollutant control

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Science of The Total Environment, 2025, 986, 179728
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High-Throughput Screening of Covalent Organic Frameworks for Carbon Capture Using Machine Learning

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Machine learning-driven computational screening of covalent organic frameworks for gas separation applications

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

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Computational Exploration of Adsorption-Based Hydrogen Storage in Mg-Alkoxide Functionalized Covalent-Organic Frameworks (COFs): Force-Field and Machine Learning Models

Yu Chen, Guobin Zhao, Sunghyun Yoon, Parsa Habibi, Chang Seop Hong, Song Li, Othonas A. Moultos, Poulumi Dey, Thijs J. H. Vlugt and Yongchul G. Chung

ACS Appl. Mater. Interfaces, 2024, 16, 61995
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Lanthanide Heterometallic MOFs with 5-Iodosophthalate Linkers: Tuning of Luminescent Properties by Varying of Metal Composition

Mikhail A. Bondarenko, Roman V. Redkin, Elizaveta A. Pilyukova, Nikita A. Korobeynikov, Alexander S. Zaguzin, Marianna I. Rakhmanova, Tatiana Ya. Guselnikova, Evgeny A. Maksimovsky, Vladimir P. Fedin and Sergey A. Adonin

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