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RESEARCH PAPER · Communications Chemistry · 2022

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

Nency P. Domingues, Seyed Mohamad Moosavi, Leopold Talirz, Kevin Maik Jablonka, Christopher P. Ireland, Fatmah Mish Ebrahim, Berend Smit

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Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOFOriginalMachine Learning and Digital Manufacturing Approaches for Solid‐State Materials Development5Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217Green Synthesis of Reticular Materials56Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective43Accelerated Discovery of Metal–Organic Frameworks for CO 2 Capture by Artificial Intelligence48Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry8Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry0Microwave‐ and Ultrasound‐Assisted Synthesis of Metal‐Organic Frameworks ( MOF ) and Covalent Organic Frameworks ( COF )8Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks46Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior15Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF4 from CH4 and N26Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74Automated synthesis and processing of functional nanomaterials: Advances and perspectives27Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions272Machine Learning-Driven Advances in Metal-Organic Framework Nanomaterials for Wastewater Treatment: Developments and Challenges27Comprehensive overview of machine learning applications in MOFs: from modeling processes to latest applications and design classifications26Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications59Defect metal-organic frameworks (D-MOFs): An engineered nanomaterial for enzyme immobilization63Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review41Applications of hot-melt extrusion in drug solubility enhancement: recent innovations and future directions2Advancing Environmental Remediation with Metal–Organic Frameworks: Perspectives on Green Synthesis, Scale‐Up Strategies, Techno‐Economic Analysis, and Life Cycle Assessment32The evolution of MOF discovery for CO2 capture: From high-throughput screening to AI design and automated laboratories9Converging pathways of microfluidic metal-organic framework and artificial intelligence: A novel research frontier3Algorithmic iterative reticular synthesis of zeolitic imidazolate framework crystals19Computer vision for high-throughput materials synthesis: a tutorial for experimentalists6Machine learning and AI empowering metal–organic frameworks: synthesis, performance prediction and therapeutic application8Metal-organic frameworks based electrocatalysts for oxygen evolution: electrochemical fundamentals, structure-activity relations, and AI-guided design5The AI Revolution in Carbon Capture, Utilization, and Storage1Sustainable Metal–Organic Framework Water Harvesters in the Artificial Intelligence Era0Engineering Redox–Transport Balance in Cobalt-Metalated Al-PMOFs for CO2 Photoreduction0

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  2. Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis

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  3. Recent advances in computational modeling of MOFs: From molecular simulations to machine learning

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    Hakan Demir, Hilal Daglar, Hasan Can Gülbalkan, Gokhan Onder Aksu, Seda Keskın

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  4. Green Synthesis of Reticular Materials

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

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  7. Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

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    S Fricke, Mia Salgado, Tamires R. Menezes, Kátilla Monique Costa Santos, Neal B. Gallagher, Ah‐Young Song, Jie‐Yu Wang, Kaitlyn E. Engler, Yang Wang, Haiyan Mao, Jeffrey A. Reimer

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  8. Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

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  9. Microwave‐ and Ultrasound‐Assisted Synthesis of Metal‐Organic Frameworks ( MOF ) and Covalent Organic Frameworks ( COF )

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

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  11. Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior

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

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  13. Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks

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    Hossein Mashhadimoslem, Mohammad Ali Abdol, Peyman Karimi, Kourosh E. Zanganeh, Ahmed Shafeen, Ali Elkamel, Milad Kamkar

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  14. Automated synthesis and processing of functional nanomaterials: Advances and perspectives

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  15. Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions

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  16. Machine Learning-Driven Advances in Metal-Organic Framework Nanomaterials for Wastewater Treatment: Developments and Challenges

    Separation and Purification Reviews · 2024-12-08

    V. Godvin Sharmila, M. Dinesh Kumar, K. Tamilarasan

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  17. Comprehensive overview of machine learning applications in MOFs: from modeling processes to latest applications and design classifications

    Journal of Materials Chemistry A · 2024-12-17

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  18. Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications

    Advanced Materials · 2025-01-07

    Bastian Achenbach, Aysu Yurduşen, Norbert Stock, Guillaume Maurin, Christian Serre

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  19. Defect metal-organic frameworks (D-MOFs): An engineered nanomaterial for enzyme immobilization

    Coordination Chemistry Reviews · 2025-02-15

    Pravin D. Patil, Niharika Gargate, Manishkumar S. Tiwari, Shamraja S. Nadar

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

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  21. Applications of hot-melt extrusion in drug solubility enhancement: recent innovations and future directions

    Drug Development and Industrial Pharmacy · 2025-10-16

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  22. Advancing Environmental Remediation with Metal–Organic Frameworks: Perspectives on Green Synthesis, Scale‐Up Strategies, Techno‐Economic Analysis, and Life Cycle Assessment

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

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  24. Converging pathways of microfluidic metal-organic framework and artificial intelligence: A novel research frontier

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    Aslam Hossain, Zhengyou Li, P.D. Kuznetsova, Ilia A. Pankin, Alexander V. Soldatov, Jun Ho Shim

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  25. Algorithmic iterative reticular synthesis of zeolitic imidazolate framework crystals

    Nature Synthesis · 2025-11-25

    Zichao Rong, Zihao Chen, Phuc Luong, Saumil P. Chheda, H. T. Nhan Luong, Zhiling Zheng, Kevin Greco, Abdullah A. Alghamdi, K. Huyen Bui, Théo Jaffrelot Inizan, Tùng Nguyen-Dang, Huy‐Hieu Pham, Dung D. Le, Joachim Sauer, Thi Viet Bac Phung, Jennifer Chayes, Christian Borgs, Mario Boley, Laurent El Ghaoui, Omar M. Yaghi

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  26. Computer vision for high-throughput materials synthesis: a tutorial for experimentalists

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  31. Engineering Redox–Transport Balance in Cobalt-Metalated Al-PMOFs for CO2 Photoreduction

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