A prediction model for CO<sub>2</sub>/CO adsorption performance on binary alloys based on machine learning

Cao, Xiaofeng; Luo, Wenjia; Liu, Huimin

doi:10.1039/d4ra00710g

RSC Adv.

2024

DOI: 10.1039/d4ra00710g

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A prediction model for CO₂/CO adsorption performance on binary alloys based on machine learning

Xiaofeng Cao,

Wenjia Luo,

Huimin Liu

Abstract: Machine-learning models were constructed to accurately predict CO2 and CO adsorption affinity on a wide range of binary alloying.

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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

show abstract

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A prediction model for CO₂/CO adsorption performance on binary alloys based on machine learning

Abstract: Machine-learning models were constructed to accurately predict CO2 and CO adsorption affinity on a wide range of binary alloying.

Cited by 1 publication

References 61 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

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A prediction model for CO2/CO adsorption performance on binary alloys based on machine learning

Abstract: Machine-learning models were constructed to accurately predict CO2 and CO adsorption affinity on a wide range of binary alloying.

Cited by 1 publication

References 61 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

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Product

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A prediction model for CO₂/CO adsorption performance on binary alloys based on machine learning