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RESEARCH PAPER · Matter · 2022

Machine-learning-assisted exploration of anion-pillared metal organic frameworks for gas separation

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Machine-learning-assisted exploration of anion-pillared metal organic frameworks for gas separationOriginalAutomated Graph Neural Networks Accelerate the Screening of Optoelectronic Properties of Metal–Organic Frameworks18Multi-level computational screening of anion-pillared metal-organic frameworks for propane and propene separation5Revealing acetylene separation performances of anion-pillared MOFs by combining molecular simulations and machine learning17Metal–organic cage as fluorescent probe for LiPF6 in lithium batteries6Introducing high concentration of hexafluorosilicate anions into an ultra-microporous MOF for highly efficient C2H2/CO2 and C2H2/C2H4 separation37Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective43Application of machine learning in MOFs for gas adsorption and separation31Machine learning aided computational exploration of metal–organic frameworks with open Cu sites for the effective separation of hydrogen isotopes9High-throughput computational screening and molecular fingerprint design of metal-organic framework adsorbents for separation of C3 components7Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and Mofs Using Machine Learning1High-Throughput Computation Evaluation of Metal–Organic Frameworks for Efficient Perfluorocarbons Recovery10High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model43Unifying mixed gas adsorption in molecular sieve membranes and MOFs using machine learning15Design principles for selective nitrogen adsorption via chelation of metals in metal organic frameworks3Discovery of metal–organic frameworks for inverse CO 2 /C 2 H 2 separation by synergizing molecular simulation and machine learning2Machine Learning‐Accelerated Design of High‐Efficient Lithium Adsorbents for Salt Lake Brines20Machine Learning‐Accelerated Design of High‐Efficient Lithium Adsorbents for Salt Lake Brines12Powder X-ray diffraction patterns based deep learning for the prediction of adsorption in metal-organic frameworks9Accelerating the discovery of stable metal–organic frameworks for ethane/ethylene separation using data mining and multitarget screening3Rational screening of butane isomers separation materials by coupling machine learning with thermodynamic mechanisms1Evolution and prospects of heterogeneous chiral catalysis6A Multitask Learning Model for Predicting Various Types of Metal–Organic Framework Stability7Accelerating the discovery and optimization of metal-organic framework materials via machine learning64Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation3Borane cage hybrid supramolecular metal-organic frameworks (BSFs): Design, synthesis and gas separation performance1The evolution of MOF discovery for CO2 capture: From high-throughput screening to AI design and automated laboratories9Alterations in Crystalline Parameters of Y 1-x Sm x BaSrCu 3 O 6+ z Compound Induced by Heat Treatment0CO 2 Capture from Flue Gas: A High-Fidelity Force Field and Machine Learning Framework for Adsorbent Discovery0Machine learning-driven design of CO2 capture materials: A focus on progress and challenges for MOFs and ILs2Emerging Trends in Machine Learning and Optimization for Membrane Gas Separation1Hierarchically porous carbon adsorbents combining high uptake with facile regeneration for aromatic recovery from diesel in a simulated moving bed process0

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  1. Automated Graph Neural Networks Accelerate the Screening of Optoelectronic Properties of Metal–Organic Frameworks

    The Journal of Physical Chemistry Letters · 2023-01-30

    Zhaosheng Zhang

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  2. Multi-level computational screening of anion-pillared metal-organic frameworks for propane and propene separation

    Separation and Purification Technology · 2023-02-07

    Xiang Zhang, Yongxin Hu, Hao Lyu, Jia Li, Teng Zhou

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  3. Revealing acetylene separation performances of anion-pillared MOFs by combining molecular simulations and machine learning

    Chemical Engineering Journal · 2023-04-01

    Hakan Demir, Seda Keskın

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  4. Metal–organic cage as fluorescent probe for LiPF6 in lithium batteries

    Green Energy & Environment · 2023-06-15

    Xi Li, Dehua Xu, Aoxuan Wang, Chengxin Peng, Xingjiang Liu, Jiayan Luo

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  5. Introducing high concentration of hexafluorosilicate anions into an ultra-microporous MOF for highly efficient C2H2/CO2 and C2H2/C2H4 separation

    Chemical Engineering Journal · 2023-11-03

    Jianjun You, Haiping Wang, Taotao Xiao, Xiao‐Yuan Wu, Lei Zhang, Can‐Zhong Lu

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  6. Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective

    Langmuir · 2023-11-03

    Hongjian Tang, Lunbo Duan, Jianwen Jiang

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  7. Application of machine learning in MOFs for gas adsorption and separation

    Materials Research Express · 2023-11-13

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

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  8. Machine learning aided computational exploration of metal–organic frameworks with open Cu sites for the effective separation of hydrogen isotopes

    Separation and Purification Technology · 2023-12-10

    Yanling Chen, Yunpan Ying, Yizhen Situ, Wenxuan Li, Jiahao Ren, Tongan Yan, Qingyuan Yang

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  9. High-throughput computational screening and molecular fingerprint design of metal-organic framework adsorbents for separation of C3 components

    Giant · 2023-12-14

    Chengzhi Cai, Lifeng Li, Yafang Guan, Xiaoshan Huang, Shiqing Ke, Wenfei Wang, Yu Li, Yujuan Yang, Hong Liang, Shuhua Li, Yufang Wu, Hanyu Gao, Zhiwei Qiao

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  10. Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and Mofs Using Machine Learning

    SSRN Electronic Journal · 2024-01-01

    Subhadeep Dasgupta, Amal RS, Prabal Kumar Maiti

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  11. High-Throughput Computation Evaluation of Metal–Organic Frameworks for Efficient Perfluorocarbons Recovery

    The Journal of Physical Chemistry C · 2024-01-04

    Jianbo Hu, Xian Suo, Lifeng Yang, Jiadong Zhu, Jianjun Zhang, Huabin Xing, Xili Cui

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  12. High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model

    Green Energy & Environment · 2024-02-03

    Xuefeng Bai, Yi Li, Ya-Bo Xie, Qiancheng Chen, Xin Zhang, Jian‐Rong Li

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  13. Unifying mixed gas adsorption in molecular sieve membranes and MOFs using machine learning

    Separation and Purification Technology · 2024-06-24

    Subhadeep Dasgupta, Amal R.S., Prabal Kumar Maiti

    Read original paper ↗ ·
  14. Design principles for selective nitrogen adsorption via chelation of metals in metal organic frameworks

    Separation and Purification Technology · 2024-09-30

    Jianbo Hu

    Read original paper ↗ ·
  15. Discovery of metal–organic frameworks for inverse CO 2 /C 2 H 2 separation by synergizing molecular simulation and machine learning

    Molecular Systems Design & Engineering · 2025-01-01

    Daohui Zhao, Mao Wang, Zhiming Zhang, Jianwen Jiang

    Read original paper ↗ ·
  16. Machine Learning‐Accelerated Design of High‐Efficient Lithium Adsorbents for Salt Lake Brines

    Angewandte Chemie International Edition · 2025-03-11

    Rui Zhang, Siying Liu, Cheng Lian, Jianguo Yu, Sen Lin

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  17. Machine Learning‐Accelerated Design of High‐Efficient Lithium Adsorbents for Salt Lake Brines

    Angewandte Chemie · 2025-03-11

    Rui Zhang, Siying Liu, Cheng Lian, Jianguo Yu, Sen Lin

    Read original paper ↗ ·
  18. Powder X-ray diffraction patterns based deep learning for the prediction of adsorption in metal-organic frameworks

    Separation and Purification Technology · 2025-05-08

    Zhenglu Yang, Jiyu Cui, Jiyu Cui, Lifeng Yang, Xian Suo, Xian Suo, Xili Cui, Huajun Chen, Huabin Xing

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  19. Accelerating the discovery of stable metal–organic frameworks for ethane/ethylene separation using data mining and multitarget screening

    Separation and Purification Technology · 2025-06-04

    Shixiang Tang, Haolun Yuan, Ruihan Wang, Dingguo Xu

    Read original paper ↗ ·
  20. Rational screening of butane isomers separation materials by coupling machine learning with thermodynamic mechanisms

    Chemical Engineering Science · 2025-07-01

    Yi Li, Bin Liu, Siqi Wang, Junyi Su, Yuanhui Ji

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  21. Evolution and prospects of heterogeneous chiral catalysis

    Microporous and Mesoporous Materials · 2025-07-05

    Yuxin Ji, Changyu Weng, Longlong Ma, Jianguo Liu

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  22. A Multitask Learning Model for Predicting Various Types of Metal–Organic Framework Stability

    Industrial & Engineering Chemistry Research · 2025-07-14

    Shengde Zhang, Zihao Wang, Hanyu Gao, Teng Zhou

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  23. 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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  24. Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation

    Chinese Journal of Chemical Engineering · 2025-10-18

    Zhikang Wu, Ying Wu, Guang Miao, Runze Chen, Lingjun Ma, Hongxia Xi, Jing Xiao

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  25. Borane cage hybrid supramolecular metal-organic frameworks (BSFs): Design, synthesis and gas separation performance

    Chinese Journal of Structural Chemistry · 2025-10-24

    Chengye Lou, Yu Hu, Yunjia Jiang, Lingyao Wang, Yuanbin Zhang

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  26. The evolution of MOF discovery for CO2 capture: From high-throughput screening to AI design and automated laboratories

    Materials Today · 2025-10-29

    Yong Long Qiu, L.Y Wang, Chang Liu, Xu Zhang, Yun Tian, Zhen Ping Zhou

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  27. Alterations in Crystalline Parameters of Y 1-x Sm x BaSrCu 3 O 6+ z Compound Induced by Heat Treatment

    Advances in Materials Science and Engineering · 2026-01-01

    Fatima Zohra Bouzit, Mohammed Bellioua, Mohamed Id Elamel, Mohammed Khenfouch, Mohamed Errai, Ahmed Tirbiyine

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  28. CO 2 Capture from Flue Gas: A High-Fidelity Force Field and Machine Learning Framework for Adsorbent Discovery

    Journal of Chemical Theory and Computation · 2026-03-02

    Yunjie Lang, Yu Wei Pan, Mei‐Yan Xu, Xin Wei, Ran Duan, Shaohuang Li, Dong Zhai, Lei Sun, Wei Deng, Li Yang

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  29. Machine learning-driven design of CO2 capture materials: A focus on progress and challenges for MOFs and ILs

    Carbon Neutral Technologies · 2026-03-29

    He Tao, Shucheng Tian, Jiali Ma, Jiahua Li, Shuaibo Huang, Zehua Wang, Haiping Liu, Binguo Zheng, Jibiao Zhang, Haiyu Tian, Fanshu Kong, Xueqing Li

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  30. Emerging Trends in Machine Learning and Optimization for Membrane Gas Separation

    Separation and Purification Reviews · 2026-06-26

    Muhammad Hamza Naveed, Ayesha Raza, Muhammad Nouman Aslam Khan, Esha Asad, Jawad Gul, Arshad Hussain

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  31. Hierarchically porous carbon adsorbents combining high uptake with facile regeneration for aromatic recovery from diesel in a simulated moving bed process

    Separation and Purification Technology · 2026-09-30

    Shibin Chen, Xinxin Li, Jian Yu, Lei Hu, Cuiting Yang, Yanze DU, Chong Peng, Steven Wang, 苗光, Jing Xiao

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