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RESEARCH PAPER · Advanced Science · 2022

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Haoxin Mai, Tu C. Le, Dehong Chen, David Alan Winkler, Rachel A. Caruso

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Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas CaptureOriginalFunctional Carbon from Nature: Biomass‐Derived Carbon Materials and the Recent Progress of Their Applications432High-throughput and machine learning approaches for the discovery of metal organic frameworks18Application of Ca-based adsorbents in fixed-bed dry flue gas desulfurization (FGD): a critical review37A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning16Meta-analysis of CO2 conversion, energy efficiency, and other performance data of plasma-catalysis reactors with the open access PIONEER database42Machine learning-assisted selection of adsorption-based carbon dioxide capture materials39Recent progress and prospects in solid acid-catalyzed CO2 desorption from amine-rich liquid45Importance of Bridging Molecular and Process Modeling to Design Optimal Adsorbents for Large-Scale CO 2 Capture13Advances in perovskite-based photovoltaics and photocatalysis: a journey fueled by ChemComm1Machine Learning Aided Design and Optimization of Thermal Metamaterials195Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives66Climate Change Through Quantum Lens: Computing and Machine Learning31Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors32Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space32Carbon-Based Materials Sources and Production Techniques0Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74Road transportation emission prediction and policy formulation: Machine learning model analysis23The Role of Nanoporous Adsorbents in the Circular Economy—Closing the Loop of Critical Materials Recovery37Polysaccharides and Composite Adsorbents in the Spotlight for Effective Agrochemical Residue Removal from Water20Machine learning for gas–solid interaction materials and devices10Sustainable carbon materials in environmental and energy applications47Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm86Application of machine learning in adsorption energy storage using metal organic frameworks: A review17A Multi-Method Approach to Analyzing MOFs for Chemical Warfare Simulant Capture: Molecular Simulation, Machine Learning, and Molecular Fingerprints9Enhancing energy materials with data-driven methods: A roadmap to long-term hydrogen energy sustainability using machine learning16A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials85A one-shot automated framework based on large language model and AutoML: Accelerating the design of porous carbon materials and carbon capture optimization5Harnessing artificial intelligence for sustainable environmental remediation a review11Machine learning perspective: Revealing deep mechanisms and new advances in adsorption and catalysis of gaseous molecules18A review on application of artificial intelligence in the construction of environmental functional materials6Review of zeolitic-based sorbents for CO2 capture: Insights into functional modifications, economic feasibility and machine learning-enhanced strategies31Machine learning-driven microfluidic deglycosylation for ultra-sensitive detection of PD-L1 extracellular vesicles1Accelerating the discovery and optimization of metal-organic framework materials via machine learning64Machine learning (ML)-assisted development of 2D green catalysts to support sustainability3Non‐Flammable Electrolytes for Safe Lithium‐, Sodium‐, and Potassium‐Ion Batteries38Tailor-made adsorbent materials on machine learning instructions for efficient purification of heavy metal contaminants in wastewater1Revealing Key Mechanisms in Multimechanism Interplay for ReO 4 – Removal: A Knowledge–Data Dual-Driven Framework for Directed Adsorbent Design1AI-driven optimization approaches of metal–organic frameworks for enhanced methane delivery0Emerging approaches for textural characterization of nanoporous materials by gas adsorption0Accelerated discovery of stable spinels for energy applications using dense neural network approach0Predicting the Maximum Loading in Zeolites for Hydroisomerization Applications: A Machine Learning Approach2Forecasting of Co 2 Emissions and Optimal Selection of Carbon Storage Based on Distance, Cost, and Capacity Metrics With a Data-Driven Pipeline0Review of adsorption-based carbon capture technologies: Advanced materials, emerging regeneration strategies and integrated applications2Nanostructured adsorbents for effective CO₂ sequestration: Design principles, mechanistic insights, and future directions0AI-Powered Air Scrubbers that Maximize SOx, NOx, and PM Capture with Hybrid Nanofluids0

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  2. High-throughput and machine learning approaches for the discovery of metal organic frameworks

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  3. Application of Ca-based adsorbents in fixed-bed dry flue gas desulfurization (FGD): a critical review

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  4. A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning

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  6. Machine learning-assisted selection of adsorption-based carbon dioxide capture materials

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    Eslam G. Al-Sakkari, Ahmed Ragab, Terry M.Y. So, Marzieh Shokrollahi, Hanane Dagdougui, Philippe Navarri, Ali Elkamel, Mouloud Amazouz

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  7. Recent progress and prospects in solid acid-catalyzed CO2 desorption from amine-rich liquid

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  8. Importance of Bridging Molecular and Process Modeling to Design Optimal Adsorbents for Large-Scale CO 2 Capture

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  14. Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space

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  15. Carbon-Based Materials Sources and Production Techniques

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

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  17. Road transportation emission prediction and policy formulation: Machine learning model analysis

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  18. The Role of Nanoporous Adsorbents in the Circular Economy—Closing the Loop of Critical Materials Recovery

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  19. Polysaccharides and Composite Adsorbents in the Spotlight for Effective Agrochemical Residue Removal from Water

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  21. Sustainable carbon materials in environmental and energy applications

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