Analysis of photocatalytic CO<sub>2</sub> reduction over MOFs using machine learning

Özsoysal, Simay; Oral, Burcu; Yıldırım, Ramazan

doi:10.1039/d3ta07001h

J. Mater. Chem. A

2024

DOI: 10.1039/d3ta07001h

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Analysis of photocatalytic CO₂ reduction over MOFs using machine learning

Simay Özsoysal,

Burcu Oral,

Ramazan Yıldırım

Abstract: Photocatalytic CO2 reduction over metal-organic frameworks (MOFs) is investigated by constructing a database from published articles and analyzed using machine learning tools to predict the total gas product yield (random...

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Catalysis in the digital age: Unlocking the power of data with machine learning

Abraham,

Jyothirmai,

Sinha

et al. 2024

WIREs Comput Mol Sci

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The design and discovery of new and improved catalysts are driving forces for accelerating scientific and technological innovations in the fields of energy conversion, environmental remediation, and chemical industry. Recently, the use of machine learning (ML) in combination with experimental and/or theoretical data has emerged as a powerful tool for identifying optimal catalysts for various applications. This review focuses on how ML algorithms can be used in computational catalysis and materials science to gain a deeper understanding of the relationships between materials properties and their stability, activity, and selectivity. The development of scientific data repositories, data mining techniques, and ML tools that can navigate structural optimization problems are highlighted, leading to the discovery of highly efficient catalysts for a sustainable future. Several data‐driven ML models commonly used in catalysis research and their diverse applications in reaction prediction are discussed. The key challenges and limitations of using ML in catalysis research are presented, which arise from the catalyst's intrinsic complex nature. Finally, we conclude by summarizing the potential future directions in the area of ML‐guided catalyst development.This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Data Science > Artificial Intelligence/Machine Learning Electronic Structure Theory > Density Functional Theory

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Catalysis in the digital age: Unlocking the power of data with machine learning

Abraham,

Jyothirmai,

Sinha

et al. 2024

WIREs Comput Mol Sci

View full text Add to dashboard Cite

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Machine learning of metal-organic framework design for carbon dioxide capture and utilization

Park,

Yoon,

Jerng

2024

Journal of CO2 Utilization

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Machine learning for a sustainable energy future

Oral,

Coşgun,

Kilic

et al. 2025

Chem. Commun.

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Analysis of photocatalytic CO₂ reduction over MOFs using machine learning

Abstract: Photocatalytic CO2 reduction over metal-organic frameworks (MOFs) is investigated by constructing a database from published articles and analyzed using machine learning tools to predict the total gas product yield (random...

Cited by 3 publications

References 49 publications

Catalysis in the digital age: Unlocking the power of data with machine learning

Catalysis in the digital age: Unlocking the power of data with machine learning

Machine learning of metal-organic framework design for carbon dioxide capture and utilization

Machine learning for a sustainable energy future

Contact Info

Product

Resources

About

Analysis of photocatalytic CO2 reduction over MOFs using machine learning

Abstract: Photocatalytic CO2 reduction over metal-organic frameworks (MOFs) is investigated by constructing a database from published articles and analyzed using machine learning tools to predict the total gas product yield (random...

Cited by 3 publications

References 49 publications

Catalysis in the digital age: Unlocking the power of data with machine learning

Catalysis in the digital age: Unlocking the power of data with machine learning

Machine learning of metal-organic framework design for carbon dioxide capture and utilization

Machine learning for a sustainable energy future

Contact Info

Product

Resources

About

Analysis of photocatalytic CO₂ reduction over MOFs using machine learning