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RESEARCH PAPER · International Journal of Hydrogen Energy · 2024

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

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Machine learning approaches for the prediction of hydrogen uptake in metal-organic-frameworks: A comprehensive reviewOriginalReturn on values of hydrogen energy transitions: A perspective on the conceptual framework10A study on first hydrogenation mechanism and microstructural features of hyper-eutectic Mg–Ni hydrogen storage alloy6Predicting the hydrogen storage capacity of alumina pillared interlayer clays using interpretable ensemble machine learning6Data-driven explainable machine learning approaches for predicting hydrogen adsorption in porous crystalline materials24A Review on Functional Materials for Hydrogen Storage21Advanced materials for solid-state hydrogen storage: A review on high-surface-area innovations34Accelerating the discovery and optimization of metal-organic framework materials via machine learning64Physics-Informed Analytical Models for Interpretable and Deployable Hydrogen Storage Prediction in MOFs1Recent advances in MOF membranes for oil-water separation: From surface and pore engineering to multifunctional applications19Hydrogen uptake prediction in porous carbon materials explained by decision tree machine learning Algorithms: From experimental data to interpretable predictions1An evaluation of hydrogen adsorption in shale reservoirs using machine learning and SHapley Additive exPlanation5Data-driven multi-objective optimization of dynamic hydrogen charging strategies for cryo-adsorptive hydrogen storage systems0Use of Machine Learning models as surrogate models for finding regions of structural properties of MOFs with high hydrogen storage capacities at room temperature and moderate pressures2Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction0A deep-learning-based investigation on CO2 capture performance of graphene oxide-based materials1Multi-objective environmental-economic optimization of rare metal recovery from phosphogypsum: Techno-economic and life cycle assessment driven by machine learning0Predicting hazardous Sb(III)/Sb(V) adsorption on biochar via an interpretable ML-DFT integrated BiMeSorb framework with experimental validation0Fundamental Insights Toward Efficient Hydrogen Storage: Materials Design Considerations in Nanostructured and Polymer–Hybrid Systems0

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  1. Return on values of hydrogen energy transitions: A perspective on the conceptual framework

    Technology in Society · 2025-01-17

    M. A. Hannan, Mahendhiran Sanggaran Nair, Pervaiz K. Ahmed, Santha Vaithilingam, Safat B. Wali, M.S. Reza, Sayem M. Abu, Pin Jern Ker, Rawshan Ara Begum, Hwai Chyuan Ong, Denny K. S. Ng, Gilsoo Jang

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  2. A study on first hydrogenation mechanism and microstructural features of hyper-eutectic Mg–Ni hydrogen storage alloy

    International Journal of Hydrogen Energy · 2025-03-25

    Da Hye Lee, Sung Yong Jung, Byeong‐Chan Suh, Young Min Kim, Ryun-Ho Kwak, Sung‐Min Park, Sung-Min Park, Jang‐Won Kang, Hyung-Ki Park, Taejun Ha

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  3. Predicting the hydrogen storage capacity of alumina pillared interlayer clays using interpretable ensemble machine learning

    International Journal of Hydrogen Energy · 2025-03-28

    Makungu Marco Madirisha, Lenganji Simwanda, Regina P. Mtei

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

    Journal of Alloys and Compounds · 2025-05-01

    Hung Vo Thanh, Zhenxue Dai, Mohammad Rahimi

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  5. A Review on Functional Materials for Hydrogen Storage

    Energy Storage · 2025-06-25

    Parag Girhe, Divya P. Barai, Bharat Apparao Bhanvase, Sandip H. Gharat

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  6. Advanced materials for solid-state hydrogen storage: A review on high-surface-area innovations

    International Journal of Hydrogen Energy · 2025-08-29

    Rama Chandra Muduli, Paresh Govind Kale

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  7. 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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  8. Physics-Informed Analytical Models for Interpretable and Deployable Hydrogen Storage Prediction in MOFs

    PRX Energy · 2025-11-03

    Alauddin Ahmed

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  9. Recent advances in MOF membranes for oil-water separation: From surface and pore engineering to multifunctional applications

    Coordination Chemistry Reviews · 2025-11-20

    Xiaolan Cao, Yujing Ji, Wenqing Li, Hongyi Gao, Xiaoqi Wang, Ge Wang

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  10. Hydrogen uptake prediction in porous carbon materials explained by decision tree machine learning Algorithms: From experimental data to interpretable predictions

    International Journal of Hydrogen Energy · 2025-11-23

    Hemanth Surya Sai Sunkara, Shravani Bhat A S, R. Namitha, Sushmitha Acharya, Selva Kumar Shekar, Krishnamurthy Sainath, Shabnam Siddiqui

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  11. An evaluation of hydrogen adsorption in shale reservoirs using machine learning and SHapley Additive exPlanation

    International Journal of Hydrogen Energy · 2026-01-01

    Weixin Zhang, Sandong Zhou, Ruibin Chen, Dameng Liu, Detian Yan

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  12. Data-driven multi-objective optimization of dynamic hydrogen charging strategies for cryo-adsorptive hydrogen storage systems

    SSRN Electronic Journal · 2026-01-01

    Linhui Zhang, Jia Zhou, Jiaxi Liu, Huiling Qin, Wenxin Zhang, Yongpeng Xia, Hailiang Chu, Yongjin Zou, Fen Xu, Lixian Sun

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  13. Use of Machine Learning models as surrogate models for finding regions of structural properties of MOFs with high hydrogen storage capacities at room temperature and moderate pressures

    Chemical Physics · 2026-04-20

    Alejandra Granja-DelRío, Francisco Hernando-Gallego, I. Cabria

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  14. Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction

    Composites Science and Technology · 2026-05-07

    Yan Hu, Senlin Tian, Yingjie Li

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  15. A deep-learning-based investigation on CO2 capture performance of graphene oxide-based materials

    Journal of environmental chemical engineering · 2026-07-09

    Peiyu Sun, Huimin Guo, Xin Liu, Changgong Meng

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  16. Multi-objective environmental-economic optimization of rare metal recovery from phosphogypsum: Techno-economic and life cycle assessment driven by machine learning

    Journal of environmental chemical engineering · 2026-08-03

    Jiangpeng Xue, Youwen Li, Lianghuan Wei, Ning Ling, Yuanchuan Ren

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  17. Predicting hazardous Sb(III)/Sb(V) adsorption on biochar via an interpretable ML-DFT integrated BiMeSorb framework with experimental validation

    ENGINEERING Environment · 2026-08-10

    Yaoshuo Zhang, Chenxi Zha, Lijie Yuan, Yanzhong Li, Yinan Dong, Dongli Li, dongli Li, Zehui Li, Zehui Li, Shengbing He, Dongyang Li, Dongyang Li

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  18. Fundamental Insights Toward Efficient Hydrogen Storage: Materials Design Considerations in Nanostructured and Polymer–Hybrid Systems

    The Chemical Record · 2026-08-23

    Fatimah Y. Alomari, Mohsin Ejaz, Shahid Ali, Safyan Akram Khan, Amal AlFawaz, Muhammad Nawaz Tahir, Muhammad Mansha

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