Ensemble Learning for Overall Power Conversion Efficiency of the All-Organic Dye-Sensitized Solar Cells

• Main Authors: Hui Li, Yanying Cui, Yunlong Liu, Wenze Li, Yue Shi, Chao Fang, Hongzhi Li, Ting Gao, Lihong Hu, Yinghua Lu
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Dye-sensitized solar cell; global/local learning ensemble; heterogeneous/homogeneous learning ensemble; machine learning; power conversion efficiency; quantitative structure activity relationship
• Online Access: https://ieeexplore.ieee.org/document/8395263/

Ensemble learning breaks the bottleneck of weak learners and is usually significantly more accurate than base learners. The overall power conversion efficiency of all-organic dye-sensitized solar cells is difficult to obtain by either calculations or experiments. To achieve high-accuracy models, various ensemble learning methods are investigated. Three types of global ensemble models, including homogeneous and heterogeneous ensembles, are constructed, which outperformed the best single base learner, a support vector machine model (MAE: 0.52; Q²: 0.76); in particular, a novel local heterogeneous ensemble model (MAE: 0.34 and Q²: 0.91) achieved high accuracy and generalization. This paper shows ensemble learning model is capable of exploring complicated quantitative structure activity relationship, where the features are distant from targets.