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RESEARCH PAPER · ACS Applied Energy Materials · 2024

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

Hossein Mashhadimoslem, Mohammad Ali Abdol, Kourosh E. Zanganeh, Ahmed Shafeen, Ali AbdulKareem AlHammadi, Milad Kamkar, Ali Elkamel

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Development of the CO 2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning AlgorithmsOriginalA machine learning feature descriptor approach: Revealing potential adsorption mechanisms for SF6 decomposition product gas-sensitive materials33Engineered molybdenum disulfide-based nanomaterials for capacitive deionization applications10High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework6A Dataset for Investigations of Amine-Impregnated Solid Adsorbent for Direct Air Capture9A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials85Machine Learning Reveals Key Adsorption Mechanisms for Oxyanions Based on Combination of Experimental and Published Literature Data15Machine learning optimized adsorption heat transformations from adsorbents screening to enhanced performances38Artificial intelligence technology in environmental research and health: Development and prospects23Machine Learning Simulating and Predicting the Adsorption Performance of Activated Carbons for Removing Methylene Blue from Wastewater12Review on computational fluid dynamics (CFD) modeling and simulation of CO2 adsorption20Artificial intelligence in heat and mass transfer in chemical engineering processes0Modeling CO2 adsorption capacity of diverse porous adsorbent materials: robust machine learning frameworks and insights from local and global explainable artificial intelligence3Two-step cavitation-enhanced waterborne CO2 Management: Aqueous capture via electrospun membranes and controlled hydrodynamic release3Unraveling CO2 capture mechanisms in porous carbons across different pressure ranges: A synergistic approach combining machine learning and molecular simulation10Comparative analysis of robust machine learning models for predicting CO2 uptake in porous liquids: Insights for environmental applications4Machine learning-driven advances in water treatment13Toward smart CO 2 capture by the synthesis of metal organic frameworks using large language models3Data-driven prediction of CO2 adsorption performance using tree-based ensemble models: Interpretability and online implementation0Machine learning frameworks to accurately compute CO2 capture in porous liquids0Machine learning-accelerated discovery of covalent organic frameworks for hydrogen peroxide photosynthesis16Explainable metaheuristic-optimized XGBoost models for accurate prediction of CO2 capture by graphene-based adsorbents0Flexible Metal–Organic Frameworks for Gas Handling Operations of CO2 and Its Isotopes: Mechanisms, Regulation Strategies and Potential Applications5Interpretable machine learning-based prediction and optimization of per- and polyfluoroalkyl substances chain length-dependent adsorption behavior on porous carbon materials with experimental validation0Machine Learning-Driven Insights Into CO 2 Adsorption in MOFs, Porous Carbon, and Porous Silica: A Comprehensive Review0Materials Informatics in Carbon Materials for Energy and Sustainability0Promising solid adsorbents for CO₂ capture: MOFs, zeolites, and POPs2Dynamic simulation of pore formation and evolution during pyrolysis: A two-dimensional visualization approach driven by gaussian random fields0AI-assisted design of metal-organic frameworks for hydrogen storage: From databases to generative models0

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  14. Unraveling CO2 capture mechanisms in porous carbons across different pressure ranges: A synergistic approach combining machine learning and molecular simulation

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  15. Comparative analysis of robust machine learning models for predicting CO2 uptake in porous liquids: Insights for environmental applications

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  16. Machine learning-driven advances in water treatment

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

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  18. Data-driven prediction of CO2 adsorption performance using tree-based ensemble models: Interpretability and online implementation

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

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  21. Explainable metaheuristic-optimized XGBoost models for accurate prediction of CO2 capture by graphene-based adsorbents

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  23. Interpretable machine learning-based prediction and optimization of per- and polyfluoroalkyl substances chain length-dependent adsorption behavior on porous carbon materials with experimental validation

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  24. Machine Learning-Driven Insights Into CO 2 Adsorption in MOFs, Porous Carbon, and Porous Silica: A Comprehensive Review

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