Citation: Jing Li, Xinyan Liu, Hong Wang, Yanjuan Sun, Fan Dong. Prediction and interpretation of photocatalytic NO removal on g-C₃N₄-based catalysts using machine learning[J]. Chinese Chemical Letters, ;2024, 35(2): 108596. doi: 10.1016/j.cclet.2023.108596 shu

Prediction and interpretation of photocatalytic NO removal on g-C₃N₄-based catalysts using machine learning

Jing Li ^a ,
Xinyan Liu ^b ,
Hong Wang ^b ,
Yanjuan Sun ^{a, *,} ,
Fan Dong ^{b, *,}

a.
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
b.
Research Center for Carbon-Neutral Environmental & Energy Technology, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China

sunyj@uestc.edu.cn

dongfan@uestc.edu.cn

Received Date: 8 February 2023
Revised Date: 28 April 2023
Accepted Date: 21 May 2023
Available Online: 26 May 2023

Figures(6)

Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement. However, great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems. Herein, a dataset of g-C₃N₄-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning (ML) model was proposed to predict the NO removal rate. The result shows that the Gradient Boosting Decision Tree (GBDT) demonstrated the greatest prediction accuracy with R² of 0.999 and 0.907 on the training and test data, respectively. The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate, in the order of importance, were catalyst characteristics > reaction process > preparation conditions. Moreover, the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features (e.g., doping ratio, flow rate, and pore volume) to the model output outcomes. This ML approach presents a pure data-driven, interpretable framework, which provides new insights into the influence of catalyst characteristics, reaction process, and preparation conditions on NO removal.
- Machine learning,
- g-C₃N₄-based catalysts,
- NO removal,
- Interpretability,
- Catalytic informatics
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Prediction and interpretation of photocatalytic NO removal on g-C3N4-based catalysts using machine learning

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通讯作者: 陈斌, bchen63@163.com

Prediction and interpretation of photocatalytic NO removal on g-C₃N₄-based catalysts using machine learning