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 (R2 value of 0.927), offering insights into the impact of these factors on AQY while facilitating the selection of efficient semiconductors. Furthermore, this research propose that efficient charge carrier separation and transfer at the bio-abiotic interface are crucial for achieving high AQY levels. This research provides guidance for selecting semiconductors suitable for productive PBSs while elucidating mechanisms underlying their enhanced efficiency.

中文翻译：

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基于集成机器学习模型构建和分析半导体光合生物杂化系统的新策略：可视化复杂机制和产量预测


由半导体-微生物混合体组成的光合生物混合系统（PBS）提供了一种将光能转化为化学能的新方法。然而，理解材料和微生物之间复杂的相互作用导致 PBS 具有高表观量子产率 (AQY) 具有挑战性。机器学习有望预测这些相互作用。为了解决这个问题，本研究采用基于随机森林、梯度提升决策树和极限梯度提升的集成学习 (ESL)，利用包含 15 个影响因素的数据集来预测 PBS 的 AQY。 ESL 模型表现出卓越的准确性和可解释性（R2 值为 0.927），可深入了解这些因素对 AQY 的影响，同时促进高效半导体的选择。此外，这项研究提出，生物-非生物界面上有效的载流子分离和转移对于实现高 AQY 水平至关重要。这项研究为选择适合高效 PBS 的半导体提供了指导，同时阐明了其提高效率的机制。