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RESEARCH PAPER · Materials Research Express · 2023

Application of machine learning in MOFs for gas adsorption and separation

Chao He Yang, Jingjing Qi, Anquan Wang, Jingyu Zha, Chao Liu, Shupeng Yao

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Application of machine learning in MOFs for gas adsorption and separationOriginalActive learning of alchemical adsorption simulations; towards a universal adsorption model15Recent advances in carboxylate-based indium( iii )–organic frameworks14Computational intelligence modelling of methylene blue adsorption by metal-organic frameworks0Recent advances of computational simulations on carbon capture in MOFs8Module-based machine learning models using sigma profiles of organic linkers to predict gaseous adsorption in metal-organic frameworks20Machine learning approaches for the prediction of hydrogen uptake in metal-organic-frameworks: A comprehensive review18Quantitative Structure–Property Relationships (QSPR) for Materials Science0Interpretable Machine learning model for predicting Ethane-Ethylene composition in binary distillation process10Application of machine learning in adsorption energy storage using metal organic frameworks: A review17Machine Learning for Gas Adsorption in Metal–Organic Frameworks: A Review on Predictive Descriptors69Tuned machine learning modeling tools and genetic algorithm for anionic dye adsorption on biomass-based activated carbons6Data-driven explainable machine learning approaches for predicting hydrogen adsorption in porous crystalline materials24Electrochemical Sensor Based on AuNPs/Cu/C for Simultaneous Detection of Acetaminophen and P‐Aminophenol4Constructing built-in electric field in heterogeneous interface of V2O5/MnFePBA for efficient oxygen evolution reaction and urea oxidation reaction6Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications11A physically motivated Machine Learning model for accurate gas adsorption predictions in nanoporous materials5Accelerating the discovery and optimization of metal-organic framework materials via machine learning64Multi-method material selection for adsorption using Bayesian approaches7Machine learning-driven advances in water treatment13A hybrid graph neural network framework with transformer-based atomic embeddings for predicting hydrogen adsorption in metal-organic frameworks0AI-driven optimization approaches of metal–organic frameworks for enhanced methane delivery0RetNeXt: A Pretrained Model for Transfer Learning Across the MOF Adsorption Space8Machine learning-driven design of high-performance activated carbons for enhanced CO2 capture3Predicting the Maximum Loading in Zeolites for Hydroisomerization Applications: A Machine Learning Approach2Gaining molecular insights into structure-performance relations of MOFs via high-throughput computational screening2Neural network descriptors for predicting adsorption and deactivation in energy storage tanks0Metal-ion-directed structural diversity in a dual-ligand system: A series of MOFs for photoelectrocatalytic water oxidation2Machine Learning-Driven Insights Into CO 2 Adsorption in MOFs, Porous Carbon, and Porous Silica: A Comprehensive Review0Accelerating the discovery of high-capacity solid-state hydrogen storage materials: A predictive machine learning framework0Machine learning-guided design of engineered metal-organic frameworks for sustainable hydrogen storage and production0Beyond Permeability Prediction: Machine Learning for Gas-Separation Membranes from Data Curation to Experimentally Validated Discovery0

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  1. Active learning of alchemical adsorption simulations; towards a universal adsorption model

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  2. Recent advances in carboxylate-based indium( iii )–organic frameworks

    Inorganic Chemistry Frontiers · 2024-01-01

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  3. Computational intelligence modelling of methylene blue adsorption by metal-organic frameworks

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  4. Recent advances of computational simulations on carbon capture in MOFs

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  5. Module-based machine learning models using sigma profiles of organic linkers to predict gaseous adsorption in metal-organic frameworks

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  6. Machine learning approaches for the prediction of hydrogen uptake in metal-organic-frameworks: A comprehensive review

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  7. Quantitative Structure–Property Relationships (QSPR) for Materials Science

    Challenges and advances in computational chemistry and physics · 2025-01-01

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  8. Interpretable Machine learning model for predicting Ethane-Ethylene composition in binary distillation process

    Thermal Science and Engineering Progress · 2025-01-08

    P Suhailam, Raju Yerolla, Chandra Shekar Besta

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  9. Application of machine learning in adsorption energy storage using metal organic frameworks: A review

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    Nokubonga P. Makhanya, Michael Kumi, Charles Mbohwa, Bilainu Obozokhai Oboirien

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  10. Machine Learning for Gas Adsorption in Metal–Organic Frameworks: A Review on Predictive Descriptors

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    I-Ting Sung, Ya-Hung Cheng, Chieh‐Ming Hsieh, Li‐Chiang Lin

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  11. Tuned machine learning modeling tools and genetic algorithm for anionic dye adsorption on biomass-based activated carbons

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  12. Data-driven explainable machine learning approaches for predicting hydrogen adsorption in porous crystalline materials

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  13. Electrochemical Sensor Based on AuNPs/Cu/C for Simultaneous Detection of Acetaminophen and P‐Aminophenol

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    Sumin Xu, Wenjing Liu, Jinjin Zhang, Jianying Qu

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  14. Constructing built-in electric field in heterogeneous interface of V2O5/MnFePBA for efficient oxygen evolution reaction and urea oxidation reaction

    International Journal of Hydrogen Energy · 2025-05-17

    Yaofeng Shi, Yaofeng Shi, Fengqin Tang, Lulu Liang, Jikui Zhu, Jianhui Jiang, Fuxi Bao, Chunhui Shi, Feng Yu, Libing Hu

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  15. Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

    International Journal of Hydrogen Energy · 2025-07-15

    Tanumoy Banerjee, Kevin Ji, Weyi Xia, Gaoyuan Ouyang, Tyler J. Del Rose, Ihor Z. Hlova, Benjamin G. Ueland, Duane D. Johnson, Cai‐Zhuang Wang, Ganesh Balasubramanian, Prashant Kumar Singh

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  16. A physically motivated Machine Learning model for accurate gas adsorption predictions in nanoporous materials

    Microporous and Mesoporous Materials · 2025-08-11

    Loukas Manitsas, George S. Fanourgakis

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  17. Accelerating the discovery and optimization of metal-organic framework materials via machine learning

    Advances in Colloid and Interface Science · 2025-09-10

    Hong Wang, Liang Yang, Deying Leng, Yanhai Du, Hao Ning

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  18. Multi-method material selection for adsorption using Bayesian approaches

    Communications Materials · 2025-10-02

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  19. Machine learning-driven advances in water treatment

    Journal of environmental chemical engineering · 2025-11-27

    Zhen Qiu, Zechao C. Zhuang, Xue Jia, Hao Li, Cheng Chen, Jinchao Shen, Bing Yu

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  20. A hybrid graph neural network framework with transformer-based atomic embeddings for predicting hydrogen adsorption in metal-organic frameworks

    Materials Today Energy · 2026-01-21

    JIANGYANG TAN, Haijian Shao, Xing Deng, YINGTAO JIANG

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  21. AI-driven optimization approaches of metal–organic frameworks for enhanced methane delivery

    Energy Conversion and Management X · 2026-01-21

    Helder R O Rocha, Sara Abou Dargham, Jimmy Romanos, Wesley Costa, Roy Roukos, Jair A. L. Silva, H. J. Wörtche

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  22. RetNeXt: A Pretrained Model for Transfer Learning Across the MOF Adsorption Space

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  23. Machine learning-driven design of high-performance activated carbons for enhanced CO2 capture

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  24. Predicting the Maximum Loading in Zeolites for Hydroisomerization Applications: A Machine Learning Approach

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  25. Gaining molecular insights into structure-performance relations of MOFs via high-throughput computational screening

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  26. Neural network descriptors for predicting adsorption and deactivation in energy storage tanks

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  27. Metal-ion-directed structural diversity in a dual-ligand system: A series of MOFs for photoelectrocatalytic water oxidation

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  28. Machine Learning-Driven Insights Into CO 2 Adsorption in MOFs, Porous Carbon, and Porous Silica: A Comprehensive Review

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  30. Machine learning-guided design of engineered metal-organic frameworks for sustainable hydrogen storage and production

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