Explainable AI-driven discovery of optimal modular architectures of CO2 reduction reactor clusters

doi:10.1007/s11705-026-2678-y

Explainable AI-driven discovery of optimal modular architectures of CO2 reduction reactor clusters

2026-06-05

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DOI：10.1007/s11705-026-2678-y

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The performance degradation of modular devices during scaling up necessitates rational design of the integration structure. However, its complex structure makes it challenging to reveal the mechanism of the effect of hierarchical multi-scale structural parameters on performance. This study proposes a data-driven framework to analyze structure-performance relationships and identify optimal scale-up patterns, using a CO2 reduction microreactor as a case study. A quantitative relationship between structure and performance is established using extreme gradient boosting tree combined with the Shapley additive explanations analysis, elucidating the regulatory mechanisms of structural parameters on performance. While a classification model is utilized to define the criteria for identifying optimal structures. Additionally, optimal scale-up design patterns under various scenarios are uncovered using K-means clustering. The results indicate that Small-sized few-stack parallel structures and large-sized single-stack structures s are the scaling-up patterns that can balance cost and performance. This approach provides important insights for the industrial scale design of modular devices.

Keywords:

modular devices

scale-up

structure-performance relationship

machine learning

Explainable AI-driven discovery of optimal modular architectures of CO2 reduction reactor clusters

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