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Data-driven insights into photocatalytic dye degradation: Ensemble learning and SHAP-based mechanistic interpretation
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DOI：10.1002/jctb.70207.png)
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Abstract
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Photocatalytic dye degradation remains difficult to predict because performance depends on a complex interplay among catalyst composition, electronic structure, and operating conditions, while the available literature is often inconsistent in experimental design and reporting. In this study, a literature curated dataset containing photocatalytic degradation experiments from 305 studies was systematically analyzed using interpretable machine learning to reveal quantitative structure–activity relationships. The dataset integrates compositional descriptors including photocatalyst type, active metal, and support materials, with physicochemical and operational variables such as bandgap, surface area, catalyst dosage, reaction time, dye concentration, and solution volume.
Keywords:
photocatalysis
machine learning
dye degradation
Random Forest
structure activity relationship
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