@article{Fan2025, 
author = {Guo-Feng Fan and Jia-Jing Qian and Li-Ling Peng and Xin-Hang Jia and Ling-Han Zuo and Jia-Can Yan and Jiang-Yan Chen and Anantkumar J. Umbarkar and Wei-Chiang Hong},
title = {Optimizing Performance Prediction of Perovskite Photovoltaic Materials by Statistical Methods-Intelligent Calculation Model},
year = {2025},
journal = {Computer Modeling in Engineering & Sciences},
volume = {145},
number = {3},
pages = {3813-3837},
keywords = {Perovskite photovoltaic cells, random forest, knowledge distillation, BiGRU-attention},
url = {https://www.sciopen.com/article/10.32604/cmes.2025.073615},
doi = {10.32604/cmes.2025.073615},
abstract = {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.}
}