TY  - EJOU
AU  - Fan, Guo-Feng 
AU  - Qian, Jia-Jing 
AU  - Peng, Li-Ling 
AU  - Jia, Xin-Hang 
AU  - Zuo, Ling-Han 
AU  - Yan, Jia-Can 
AU  - Chen, Jiang-Yan 
AU  - Umbarkar, Anantkumar J. 
AU  - Hong, Wei-Chiang 

TI  - Optimizing Performance Prediction of Perovskite Photovoltaic Materials by Statistical Methods-Intelligent Calculation Model
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 145
IS  - 3
SN  - 1526-1506

AB  - Accurate prediction of perovskite photovoltaic materials’ optoelectronic properties is crucial for developing efficient and stable materials, advancing solar technology. To address poor interpretability, high computational complexity, and inaccurate predictions in relevant machine learning models, this paper proposes a novel methodology. The technical route of this paper mainly centers on the random forest-knowledge distillation-bidirectional gated recurrent unit with attention technology (namely RF-KD-BIGRUA), which is applied in perovskite photovoltaic materials. Primarily, it combines random forest to quantitatively assess feature importance, selecting variables with significant impacts on photoelectric conversion efficiency. Subsequently, statistical techniques analyze the weight distribution of variables influencing power conversion efficiency (PCE, %) to extract key features. In the model optimization phase, knowledge distillation transfers features from complex teacher models to student models, enhancing prediction accuracy. Additionally, Bidirectional Gated Recurrent Unit with Attention technology (BiGRU-Attention) is introduced to further optimize predictive performance while substantially reducing computational costs. The results demonstrate that integrating statistical techniques into intelligent optimization models can quantify photovoltaic system uncertainties and reduce prediction errors before experimental fabrication, enabling efficient pre-fabrication screening of perovskite materials that meet energy-storage criteria and providing accurate guidance for material selection.
KW  - Perovskite photovoltaic cells; random forest; knowledge distillation; BiGRU-attention

DO  - 10.32604/cmes.2025.073615