The convergence of data science and catalysis: a comprehensive review of machine learning applications in accelerating selective catalytic reduction (SCR) catalyst design and performance prediction

Cheng Zheng Tong; Qingling Liu; Hu Wang; Mingfa Yao

doi:10.1039/d5en00610d

January 1, 2026Environmental Science Nano

The convergence of data science and catalysis: a comprehensive review of machine learning applications in accelerating selective catalytic reduction (SCR) catalyst design and performance prediction

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Key Points

The aim is to explore how machine learning can enhance the design and performance of SCR catalysts.
Review existing literature on machine learning applications in SCR
Analyze the integration of data science with catalysis
Identify key trends and innovations in catalyst design and prediction
Machine learning significantly reduces the time and cost involved in catalyst development.
Identified new approaches for predicting catalyst performance under various conditions.
The convergence of data science and SCR enhances the design process and efficacy of catalysts.

Implication

This review highlights machine learning applications improving SCR catalyst design and performance prediction, indicating significant advancements in catalyst technology.

Abstract

Selective Catalytic Reduction (SCR) represents the dominant technology for mitigating nitrogen oxide (NOx) emissions, yet designing superior catalysts remains a complex challenge. Machine Learning (ML) offers transformative potential to accelerate...

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Cite This Study

Tong et al. (2026) studied this question.

synapsesocial.com/papers/699a9d50482488d673cd32c1 https://doi.org/10.1039/d5en00610d

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The convergence of data science and catalysis: a comprehensive review of machine learning applications in accelerating selective catalytic reduction (SCR) catalyst design and performance prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

The convergence of data science and catalysis: a comprehensive review of machine learning applications in accelerating selective catalytic reduction (SCR) catalyst design and performance prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

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