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RESEARCH PAPER · ACS Nano · 2024

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

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

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Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic FrameworksOriginalDevelopment of the CO 2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms28Computational design of Metal-Organic Frameworks for sustainable energy and environmental applications: Bridging theory and experiment6Closing the Loop in the Carbon Cycle: Enzymatic Reactions Housed in Metal–Organic Frameworks for CO2 Conversion to Methanol3Data‐Driven Innovation in Metal‐Organic Frameworks Photocatalysis: Bridging Gaps for CO 2 Capture and Conversion with FAIR Principles4AI-driven advances in metal–organic frameworks: from data to design and applications47Machine learning-accelerated exploration on element doping-triggering material performance improvement for energy conversion and storage applications22Machine Learning-Driven Prediction and Design of High-Performance Metal–Organic Frameworks for Co₂ Capture0Prognosis of simplified metal and organic sites resembling a quasi-MOF structure for hydrogen storage: investigation and insight0Sustainable carbon materials in environmental and energy applications47Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications59Application of machine learning in adsorption energy storage using metal organic frameworks: A review17Artificial Intelligence (AI) Can Advance Plastic Sustainability and Circular Economy11Zinc Single-Atom Nanozyme As Carbonic Anhydrase Mimic for CO2 Capture and Conversion32Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials13Architecting Metal–Organic Frameworks at Molecular Level toward Direct Air Capture103High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework6Review and analysis of porous adsorbents for effective CO2 capture70Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications66The Application of Digital Technologies in Carbon Emission Reduction in China's Energy and Power Industry0A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials85Machine-Learning-Guided Screening of Advantageous Solvents for Solid Polymer Electrolytes in Lithium Metal Batteries24Tunable CO2 capture in N-ethylethylenediamine functionalized Mg2-MOF-74: unraveling the role of diamine basicity in reactivity and adsorption capacity7Machine Learning Reveals Key Adsorption Mechanisms for Oxyanions Based on Combination of Experimental and Published Literature Data15Machine Learning-Guided Conductivity Prediction in 2D Organic Metal Chalcogenides for Accelerated Electromagnetic Wave Absorber Design8Facile fabrication of MnCO3 and covalent organic framework on the surface of MXene with sodium alginate hydrogel for effective photocatalytic eradication of disinfected byproducts7Metal–Organic Frameworks for PFAS Remediation and Sensing: From Molecular Design to Real-World Implementation46Superhydrophobic ZIF-67 sponge: A breakthrough in efficient oil-water separation2Decoding solute-vacancy binding in transition metals via first-principles and machine learning0Metal-organic frameworks for CO2 capture: Tailoring structure and function through modification strategies29High-throughput DFT screening of single-metal and high-entropy MOF-74 for selective CO2/N2 separation and H2 storage9Advances in metal-based nanomedicine: From basic science to clinical implications32Machine Learning‐Driven Prediction and Design of High‐Performance Metal‐Organic Frameworks for CO 2 Capture1A perspective on hydrate-based CO 2 captures for industrial gas mixtures0Interpretable machine learning assisted screening of metal-organic frameworks for methane adsorption mechanism exploration under low and high pressures2Two-step cavitation-enhanced waterborne CO2 Management: Aqueous capture via electrospun membranes and controlled hydrodynamic release3Intelligent screening of porous materials: A review of active-learning approaches in MOF research6Carbon capture from biogas using adsorbents0Toward smart CO 2 capture by the synthesis of metal organic frameworks using large language models3Toward high entropy material discovery for energy applications using computational and machine learning methods8Automated machine learning predicting SO₂ and CO₂ adsorption and separation in metal organic frameworks0Direct utilization of metal–organic frameworks in electrochemical reduction of CO 2 : from structural design to performance optimization0Integrating Quantum Computing into Sustainable Carbon-Capture Materials Research: Opportunities and Perspectives0Machine-learning-guided design of MOF-based electrocatalysts for sustainable ammonia production19Precise atomic/molecular design and functional regulation of dynamic cross-scale electrocatalytic interfaces0Metal-organic frameworks: Role of artificial intelligence and machine learning algorithms for efficient discovery, design, synthesis and prediction of CO2 capture capacity - A state of art review18A Transferable Force Field for Simulating Adsorption in Metal–Organic Frameworks with Open Metal Sites Based on the 12–6–4 Lennard-Jones Potential2Structural modifications in metal–organic frameworks for CO2 adsorption and sensing through sustainable synthesis and emerging designs4Deep learning-assisted efficient design of biomimetic fluorescent dielectric elastomers0A deep learning-assisted SMART label for food freshness monitoring and prediction under variable lighting conditions1Machine learning frameworks to accurately compute CO2 capture in porous liquids0MOF-74: A leading contender for direct air capture, navigating the path from promise to practicality1A comprehensive review on development of framework- (MOF/COF/POP) based materials for catalytic conversion of carbon dioxide from dilute/flue gas sources to high-value chemicals10A review on emerging applications of metal organic frameworks and the growing impact of artificial intelligence and machine learning in MOFs design and development6Metal-organic frameworks and their derivatives for carbon dioxide conversion to ethylene and ethanol2MOFBuilder: automated end-to-end modeling of MOF dynamics for high-throughput screening1Imidazole-based MOFs for photo/electrocatalytic CO2 conversion: Controllable construction, mechanistic investigation, and machine learning1Integrated Machine Learning and Molecular Dynamics for Functional Nanoparticle Design: Synthesis, Characterization, Force-Field Development, and Property Prediction2Aminoethylethanolamine-Modified Silica Nanoparticles for CO 2 Capture4Prediction of Carbon Dioxide Adsorption Performance of Covalent Organic Frameworks (COFs) Based on Machine Learning: Identification of Key Factors and Model Deployment1Machine Learning-Driven Insights Into CO 2 Adsorption in MOFs, Porous Carbon, and Porous Silica: A Comprehensive Review0Porous Organic Polymers: From Molecular Design to Scalable Technologies2Promising solid adsorbents for CO₂ capture: MOFs, zeolites, and POPs2Metal organic framework-derived hollow structures for environmental remediation and wastewater treatment: The nexus of experimental, artificial intelligence, and machine learning approaches0High-efficiency CO2 adsorption: unveiling the mechanism of porous carbons derived from TEPA-modified chitosan0Humidity‑aware agro‑waste biochar–MOF adsorbents for solar‑regenerated CO₂ capture: A mechanistic circular‑economy design framework0Coupling Molecular Simulation and Machine Learning for CO2 Adsorption Prediction on Defective and N/O/S-Doped Graphene Models: Insights into Feature Importance and Synergistic Effects0A Novel Aliphatic Zr‐Based Nanocluster With S‐Shaped Channel for CO 2 Adsorption and Separation Under Humid Condition0Machine learning approach for MOF-based photocatalytic CO2 reduction to fuels0Moisture-Activated, Low-Temperature Regeneratable Hydrogels for Cost-Effective Carbon Dioxide Capture and Water Harvesting0AI-assisted design of metal-organic frameworks for hydrogen storage: From databases to generative models0Machine learning-driven high-throughput prediction of Hg0 capture in MOFs and decipherment of structure-property relationships0Nanostructured transition aluminas: advanced and environmentally friendly synthesis, structure engineering, and functional applications0A Review of Photochemical and Electrochemical CO2 Valorization: Current Progress and Future Perspectives for Re-envisioning Carbon0

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  1. Development of the CO 2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms

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  2. Computational design of Metal-Organic Frameworks for sustainable energy and environmental applications: Bridging theory and experiment

    Materials Science and Engineering B · 2024-11-01

    Qiang Ma, Yi Wang, Xianglong Zhang, Qianchen Zhao, Jinjun guo, Jiahu Guo, Xu Ren, Jin Liang Huang, Yingjie Zhang, Yonghong Xie, Jiming Hao

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  3. Closing the Loop in the Carbon Cycle: Enzymatic Reactions Housed in Metal–Organic Frameworks for CO2 Conversion to Methanol

    Applied Biochemistry and Biotechnology · 2024-11-26

    Praise Moyo, Gift Mehlana, Banothile C. E. Makhubela, Piwai Tshuma, Evernice S Chikukwa

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  4. Data‐Driven Innovation in Metal‐Organic Frameworks Photocatalysis: Bridging Gaps for CO 2 Capture and Conversion with FAIR Principles

    Advanced Energy and Sustainability Research · 2024-12-30

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  5. 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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  6. Machine learning-accelerated exploration on element doping-triggering material performance improvement for energy conversion and storage applications

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  7. Machine Learning-Driven Prediction and Design of High-Performance Metal–Organic Frameworks for Co₂ Capture

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  8. Prognosis of simplified metal and organic sites resembling a quasi-MOF structure for hydrogen storage: investigation and insight

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  9. Sustainable carbon materials in environmental and energy applications

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

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

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  11. 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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  12. Artificial Intelligence (AI) Can Advance Plastic Sustainability and Circular Economy

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  13. Zinc Single-Atom Nanozyme As Carbonic Anhydrase Mimic for CO2 Capture and Conversion

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  14. Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials

    Communications Materials · 2025-02-02

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  15. Architecting Metal–Organic Frameworks at Molecular Level toward Direct Air Capture

    Journal of the American Chemical Society · 2025-02-07

    Zi‐Ming Ye, Yi Xie, Kent O. Kirlikovali, Shengchang Xiang, Omar K. Farha, Banglin Chen

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  16. High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework

    Journal of Chemical Information and Modeling · 2025-02-14

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  17. Review and analysis of porous adsorbents for effective CO2 capture

    Renewable and Sustainable Energy Reviews · 2025-03-16

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  18. Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

    Environmental Science & Technology · 2025-03-30

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  19. The Application of Digital Technologies in Carbon Emission Reduction in China's Energy and Power Industry

    Net Zero · 2025-04-23

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  20. A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials

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  21. Machine-Learning-Guided Screening of Advantageous Solvents for Solid Polymer Electrolytes in Lithium Metal Batteries

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  22. Tunable CO2 capture in N-ethylethylenediamine functionalized Mg2-MOF-74: unraveling the role of diamine basicity in reactivity and adsorption capacity

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  23. Machine Learning Reveals Key Adsorption Mechanisms for Oxyanions Based on Combination of Experimental and Published Literature Data

    Environmental Science & Technology · 2025-05-28

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  24. Machine Learning-Guided Conductivity Prediction in 2D Organic Metal Chalcogenides for Accelerated Electromagnetic Wave Absorber Design

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  25. Facile fabrication of MnCO3 and covalent organic framework on the surface of MXene with sodium alginate hydrogel for effective photocatalytic eradication of disinfected byproducts

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  26. Metal–Organic Frameworks for PFAS Remediation and Sensing: From Molecular Design to Real-World Implementation

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  27. Superhydrophobic ZIF-67 sponge: A breakthrough in efficient oil-water separation

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  28. Decoding solute-vacancy binding in transition metals via first-principles and machine learning

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  29. Metal-organic frameworks for CO2 capture: Tailoring structure and function through modification strategies

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  30. High-throughput DFT screening of single-metal and high-entropy MOF-74 for selective CO2/N2 separation and H2 storage

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  31. Advances in metal-based nanomedicine: From basic science to clinical implications

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  32. Machine Learning‐Driven Prediction and Design of High‐Performance Metal‐Organic Frameworks for CO 2 Capture

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  33. A perspective on hydrate-based CO 2 captures for industrial gas mixtures

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  34. Interpretable machine learning assisted screening of metal-organic frameworks for methane adsorption mechanism exploration under low and high pressures

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  35. Two-step cavitation-enhanced waterborne CO2 Management: Aqueous capture via electrospun membranes and controlled hydrodynamic release

    Journal of environmental chemical engineering · 2025-11-13

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  36. Intelligent screening of porous materials: A review of active-learning approaches in MOF research

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  37. Carbon capture from biogas using adsorbents

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  38. Toward smart CO 2 capture by the synthesis of metal organic frameworks using large language models

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  39. Toward high entropy material discovery for energy applications using computational and machine learning methods

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  40. Automated machine learning predicting SO₂ and CO₂ adsorption and separation in metal organic frameworks

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  41. Direct utilization of metal–organic frameworks in electrochemical reduction of CO 2 : from structural design to performance optimization

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  42. Integrating Quantum Computing into Sustainable Carbon-Capture Materials Research: Opportunities and Perspectives

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  43. Machine-learning-guided design of MOF-based electrocatalysts for sustainable ammonia production

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  44. Precise atomic/molecular design and functional regulation of dynamic cross-scale electrocatalytic interfaces

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  45. Metal-organic frameworks: Role of artificial intelligence and machine learning algorithms for efficient discovery, design, synthesis and prediction of CO2 capture capacity - A state of art review

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  46. A Transferable Force Field for Simulating Adsorption in Metal–Organic Frameworks with Open Metal Sites Based on the 12–6–4 Lennard-Jones Potential

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  47. Structural modifications in metal–organic frameworks for CO2 adsorption and sensing through sustainable synthesis and emerging designs

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  48. Deep learning-assisted efficient design of biomimetic fluorescent dielectric elastomers

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  49. A deep learning-assisted SMART label for food freshness monitoring and prediction under variable lighting conditions

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  50. Machine learning frameworks to accurately compute CO2 capture in porous liquids

    Chemical Papers · 2026-02-20

    Farag M. A. Altalbawy, Soumya Vettiyatil Menon, Anupam Yadav, Subhashree Ray, Kapil Ghai, Atreyi Pramanik, Shirin Shomurotova, Gunjan Garg, Ahmad Alkhayyat, Ahmad Abumalek

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