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RESEARCH PAPER · Chemical Engineering Journal · 2024

Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks

Ruolin Du, Ruiqi Xin, Han Wang, Wenkai Zhu, Rui Li, Wei Liu

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Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworksOriginalBiomass: The accelerator for moving MOFs to practical applications95Chemical-guided screening of top-performing metal–organic frameworks for hydrogen storage: An explainable deep attention convolutional model19Machine learning approaches for the prediction of hydrogen uptake in metal-organic-frameworks: A comprehensive review18Rapid analysis of salivary glucose content using MOF/MIPs biomimetic microfluidic paper chips22Climate friendly MOFs synthesis for drug delivery systems by integrating AI, intelligent manufacturing, and quantum solutions in industry 6.0 sustainable approach27AI-driven advances in metal–organic frameworks: from data to design and applications47Emerging MOF-based antibiotic detection methods in water environments: Recent advances, challenges, and prospects17Evaluating the path to sustainability: SWOT analysis of safe and sustainable by design approaches for metal–organic frameworks48Generation and π-phase-induced oscillations of multi-soliton molecular complexes in ultrafast fiber lasers based on MOF-253@Au35Artificial Intelligence in Metal–Organic Frameworks from 2013 to 2024: A Bibliometric Analysis10CO2 Adsorption Using Graphene-Based Materials: A Review25Application of machine learning in adsorption energy storage using metal organic frameworks: A review17Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review16Machine learning assisted biomimetic flexible SERS sensor from seashells for pesticide classification and concentration prediction64Machine learning-assisted dispersion engineering for predicting ultra-flat soliton microcombs3Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review41Discovery of metal-organic frameworks for efficient NF3/N2 separation by integrating high-throughput computational screening, machine learning, and experimental validation9Machine-Learning-Assisted Synthesis of Bimetallic Metal–Organic Frameworks for the Optimized Oxygen Evolution Reaction21Metal-organic framework membranes for lithium extraction from Mg/Li brines: Advances in design, machine learning-driven optimization, and scalability challenges11Machine learning-guided design of metal-organic frameworks for efficient H2S/CH4 separation4Optimization of defect regulation parameters in CsPbI3 perovskite solar cells via machine learning-assisted response surface methodology9Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research54A comprehensive review on exploring the potential and behaviour of graphene-based materials for CO2 capture16Applying stacked machine learning models to guide electrochemical oxidation of antibiotics: Key parameter identification and process optimization insights7Interface and surface engineering: The nexus of MXenes, MOFs, and AI in hybrid material design for energy storage/conversion27Performance Evaluation of Decision Tree, Random Forest, and XGBoost for Predicting Cycle Life in MOF-Based Battery Electrodes0Artificial intelligence-based prediction of shear modulus and damping ratio of recycled tire rubber–soil mixtures for sustainable engineering applications1Efficient and explainable transformer-based language models for property prediction in metal-organic frameworks3Machine learning-assisted photoelectrochemical/fluorescence dual-mode sensor for ultra-sensitive glyphosate detection7Machine learning-driven optimization of catalytic plastic upcycling: Semi-supervised imputation and inverse experimental design0Machine learning accelerates the development of metal–organic frameworks0Langmuir and Langmuir–Blodgett Monolayers from 20 nm Sized Crystals of the Metal–Organic Framework MIL-101(Cr)3Data-driven and machine learning to exploring the metal–organic frameworks for methane adsorption0Introduction to Machine Learning for chemical engineers0Extracting and reconstructing knowledge in materials science literature using large language models10Nondestructive detection of carotenoids in chrysanthemum and identification of drought using hyperspectral and machine learning1Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation15Advancing screening of aquatic persistent, mobile, and toxic substances via integration of graph convolutional networks and multitask learning0Bridging the gap between empirical and rational design: machine learning framework for lithium ion-sieve performance prediction2Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction0Machine learning-assisted coordination-driven ratiometric fluorescent hydrogel sensor based on hydrogen-bonded organic frameworks for rapid detection of dithiocarbamates0Interpretable Machine Learning Unveils Hydroxyl/Amino Synergy and Guides Discovery of Optimal MOF Photocatalysts for Hydrogen Evolution5MCLFormer: a text-driven deep learning framework for predicting CO2 adsorption in metal-organic frameworks0A systematic review of machine learning in aqueous pollutant adsorption, from prediction to mechanistic interpretation and adsorbent design0Machine learning-derived decatungstate photocatalytic CH functionalization: from machine learning to predictive modeling1Machine learning-driven high-throughput prediction of Hg0 capture in MOFs and decipherment of structure-property relationships0

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  1. Biomass: The accelerator for moving MOFs to practical applications

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    Rongfu Peng, Ruiqi Xin, Dujuan Wu, Wenkai Zhu, Yingyi Li, Shangru Zhai

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  2. Chemical-guided screening of top-performing metal–organic frameworks for hydrogen storage: An explainable deep attention convolutional model

    Chemical Engineering Journal · 2024-09-10

    Abdulrahman H. Ba-Alawi, Sridhar Palla, Seshagiri Rao Ambati, Hai-Tra Nguyen, SangYoun Kim, ChangKyoo Yoo

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

    International Journal of Hydrogen Energy · 2024-12-13

    Aryan Anil Yamde, Vikesh Gurudas Lade, Ankush Babarao Bindwal, Manishkumar S. Tiwari, Ramesh Pandharinath Birmod

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  4. Rapid analysis of salivary glucose content using MOF/MIPs biomimetic microfluidic paper chips

    Chemical Engineering Journal · 2024-12-27

    Ningning Li, Ningning Li, Ying Mei Zhou, Hao Sun, Nan Wang, Nan Yang, Peiyao Ren, Lanjun Fu, Yiming Zhang, Yanjie Li, Yanjie Li, Juan Jin

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  5. Climate friendly MOFs synthesis for drug delivery systems by integrating AI, intelligent manufacturing, and quantum solutions in industry 6.0 sustainable approach

    Toxicology Research · 2024-12-31

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  6. AI-driven advances in metal–organic frameworks: from data to design and applications

    Chemical Communications · 2025-01-01

    Yuhang Song, Jiakai Li, Dongzhi Chi, Zhengtao Xu, Jie Liu, Mingxi Chen, Ziyu Wang

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  7. Emerging MOF-based antibiotic detection methods in water environments: Recent advances, challenges, and prospects

    Water Cycle · 2025-01-01

    Kexin Zhao, Xinfei Li, Cuizhu Sun, Lingyun Chen, Fengmin Li

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  8. Evaluating the path to sustainability: SWOT analysis of safe and sustainable by design approaches for metal–organic frameworks

    Green Chemistry · 2025-01-01

    Pankti Dhumal, Prathmesh Bhadane, Bashiru Ibrahim, Swaroop Chakraborty

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  9. Generation and π-phase-induced oscillations of multi-soliton molecular complexes in ultrafast fiber lasers based on MOF-253@Au

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    Zhi‐Zeng Si, Long-Fei Ren, Dalei Wang, Zhen‐Tao Ju, Xuepeng Wang, Yue‐Yue Wang, Wei Liu, Chao‐Qing Dai

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  10. Artificial Intelligence in Metal–Organic Frameworks from 2013 to 2024: A Bibliometric Analysis

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  11. CO2 Adsorption Using Graphene-Based Materials: A Review

    Arabian Journal for Science and Engineering · 2025-01-11

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

    Journal of Energy Storage · 2025-01-13

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

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  13. Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review

    Next Materials · 2025-02-10

    Mir Mohammad Ali, Md. Arif Hossen, Azrina Abd Aziz

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  14. Machine learning assisted biomimetic flexible SERS sensor from seashells for pesticide classification and concentration prediction

    Chemical Engineering Journal · 2025-02-19

    Yu Wan, Wei Q. Qian, Hao Sun, Hanzhaobing Wu, Yimin Zhou, Changwei Bi, Jitao Li, Lili Li, Bin Liu, Dalei Wang, Xiaoyan Wang, Chao Wang, Wei Liu

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  15. Machine learning-assisted dispersion engineering for predicting ultra-flat soliton microcombs

    Optics Communications · 2025-02-22

    Yixuan Xiang, Biyan Zhan, Haoxuan Zhang, Xianwen Liu

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  16. Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review

    Industrial & Engineering Chemistry Research · 2025-02-24

    Yiming Zhao, Yongjia Zhao, Jian Wang, Zhuo Wang

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  17. Discovery of metal-organic frameworks for efficient NF3/N2 separation by integrating high-throughput computational screening, machine learning, and experimental validation

    Separation and Purification Technology · 2025-03-12

    Yanjing He, Zhi Fang, Wenjuan Xue, Tongan Yan, Shitong Zhang, Zhengqing Zhang, Zhengqing Zhang, Hongliang Huang, Chongli Zhong

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  18. Machine-Learning-Assisted Synthesis of Bimetallic Metal–Organic Frameworks for the Optimized Oxygen Evolution Reaction

    ACS Applied Materials & Interfaces · 2025-04-17

    Farhan Zafar, Salah M. El‐Bahy, Abdul Sami, Sadaf Ul Hassan, Naeem Akhtar, Adel Qlayel Alkhedaide, Hailing Ma, Yao Tong, Shuaifei Zhao

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  19. Metal-organic framework membranes for lithium extraction from Mg/Li brines: Advances in design, machine learning-driven optimization, and scalability challenges

    Separation and Purification Technology · 2025-04-17

    Zhongyu Du, Guirong Bao

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  20. Machine learning-guided design of metal-organic frameworks for efficient H2S/CH4 separation

    Journal of Membrane Science · 2025-05-09

    Yanjing He, Rongmei Han, Min Wang, Zhengqing Zhang, Chongli Zhong

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  21. Optimization of defect regulation parameters in CsPbI3 perovskite solar cells via machine learning-assisted response surface methodology

    Renewable Energy · 2025-05-09

    Cong Shen, Jiaqi Zhou, Tengling Ye, Peixia Yang, Lijie Sun, Guanying Chen

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  22. Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research

    Journal of the American Chemical Society · 2025-06-24

    Aydın Özcan, François‐Xavier Coudert, Sven M. J. Rogge, Greta Heydenrych, Dong Fan, Antonios P. Sarikas, Seda Keskın, Guillaume Maurin, George E. Froudakis, Stefan Wuttke, İlknur Eruçar

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  23. A comprehensive review on exploring the potential and behaviour of graphene-based materials for CO2 capture

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  24. Applying stacked machine learning models to guide electrochemical oxidation of antibiotics: Key parameter identification and process optimization insights

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  25. Interface and surface engineering: The nexus of MXenes, MOFs, and AI in hybrid material design for energy storage/conversion

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  26. Performance Evaluation of Decision Tree, Random Forest, and XGBoost for Predicting Cycle Life in MOF-Based Battery Electrodes

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  27. Artificial intelligence-based prediction of shear modulus and damping ratio of recycled tire rubber–soil mixtures for sustainable engineering applications

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  28. Efficient and explainable transformer-based language models for property prediction in metal-organic frameworks

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  29. Machine learning-assisted photoelectrochemical/fluorescence dual-mode sensor for ultra-sensitive glyphosate detection

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  30. Machine learning-driven optimization of catalytic plastic upcycling: Semi-supervised imputation and inverse experimental design

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  31. Machine learning accelerates the development of metal–organic frameworks

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  32. Langmuir and Langmuir–Blodgett Monolayers from 20 nm Sized Crystals of the Metal–Organic Framework MIL-101(Cr)

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    Asen Dimov, George R. Ivanov, Leonard Keil, Andreas Terfort, Jinxuan Liu, Velichka Strijkova

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  33. Data-driven and machine learning to exploring the metal–organic frameworks for methane adsorption

    New Journal of Chemistry · 2026-01-01

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  34. Introduction to Machine Learning for chemical engineers

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  36. Nondestructive detection of carotenoids in chrysanthemum and identification of drought using hyperspectral and machine learning

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  37. Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation

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  39. Bridging the gap between empirical and rational design: machine learning framework for lithium ion-sieve performance prediction

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

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  41. Machine learning-assisted coordination-driven ratiometric fluorescent hydrogel sensor based on hydrogen-bonded organic frameworks for rapid detection of dithiocarbamates

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  46. Machine learning-driven high-throughput prediction of Hg0 capture in MOFs and decipherment of structure-property relationships

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