Citation: Zeai Huang, Rui Deng, Ruiyang Zhang, Renze Li, Sinan Liu, Jundao Wu, Rustem Zairov, Oleg G. Sinyashin, Ying Zhou. Interpretable machine learning unravels synergistic effects in photothermal CO₂ methanation[J]. Chinese Chemical Letters, ;2026, 37(12): 111909. doi: 10.1016/j.cclet.2025.111909 shu

Interpretable machine learning unravels synergistic effects in photothermal CO₂ methanation

Zeai Huang ^{a, b, *,} ,
Rui Deng ^b ,
Ruiyang Zhang ^b ,
Renze Li ^b ,
Sinan Liu ^b ,
Jundao Wu ^c ,
Rustem Zairov ^{c, d} ,
Oleg G. Sinyashin ^d ,
Ying Zhou ^{a, b, *,}

a.
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
b.
School of New Energy and Materials, Southwest Petroleum University, Chengdu 610500, China
c.
Institute of Chemistry, Kazan Federal University, Kazan 420008, Russia
d.
Arbuzov Institute of Organic and Physical Chemistry, FRC Kazan Scientific Center, Russian Academy of Sciences, Kazan 420088, Russia

zeai.huang@swpu.edu.cn

yzhou@swpu.edu.cn

Received Date: 2 July 2025
Revised Date: 22 September 2025
Accepted Date: 27 September 2025
Available Online: 28 September 2025

Figures(8)

The efficient photothermal catalytic methanation of CO₂ offers a sustainable approach to carbon valorization, yet its practical implementation is hindered by suboptimal methane yields due to the complex interplay of multi-physical parameters. In this work, a comprehensive dataset was assembled from the literature to capture the diversity of reaction conditions, catalyst properties, and metal loading elements. Fourteen machine learning models were systematically compared for methane yield prediction, with CatBoost outperforming others (test R² = 0.946), demonstrating both exceptional accuracy and generalization. Interpretable modeling via SHapley Additive exPlanations (SHAP) and partial dependence analyses identified reaction temperature, pore size, gas flow rate, and Ru loading as critical factors, with pronounced nonlinear and synergistic effects. Notably, bivariate analysis uncovered optimal parameter regimes inaccessible through traditional uni-variable experimentation. This study provides a robust, interpretable machine-learning framework for mechanistically deciphering and optimizing photothermal CO₂ methanation, paving the way for rational catalyst and process development in green energy applications.
- Photothermal,
- CO₂ methanation,
- Machine learning,
- Model interpretability,
- Catalyst design
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Interpretable machine learning unravels synergistic effects in photothermal CO2 methanation

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通讯作者: 陈斌, bchen63@163.com

Interpretable machine learning unravels synergistic effects in photothermal CO₂ methanation