New Strategies for constructing and analyzing semiconductor photosynthetic biohybrid systems based on ensemble Machine learning Models: Visualizing complex mechanisms and yield prediction.

Photosynthetic biohybrid systems (PBSs) composed of semiconductor-microbial hybrids provide a novel approach for converting light into chemical energy. However, comprehending the intricate interactions between materials and microbes that lead to PBSs with high apparent quantum yields (AQY) is challenging. Machine learning holds promise in predicting these interactions. To address this issue, this study employs ensemble learning (ESL) based on Random Forest, Gradient Boosting Decision Tree, and eXtreme Gradient Boosting to predict AQY of PBSs utilizing a dataset comprising 15 influential factors. The ESL model demonstrates exceptional accuracy and interpretability (R

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Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.