Optimizing Performance Prediction of Perovskite Photovoltaic Materials by Statistical Methods-Intelligent Calculation Model

Guo-Feng Fan^1,2, Jia-Jing Qian¹, Li-Ling Peng¹, Xin-Hang Jia¹, Ling-Han Zuo¹, Jia-Can Yan¹, Jiang-Yan Chen¹, Anantkumar J. Umbarkar³, Wei-Chiang Hong^4,*
1 School of Mathematics & Statistics, Ping Ding Shan University, Pingdingshan, 467000, China
2 Yaoshan Lab, Ping Ding Shan University, Pingdingshan, 467000, China
3 Department of Information Technology, Walchand College of Engineering, Sangli, 416415, Maharashtra, India
4 College of Shipbuilding Engineering, Harbin Engineering University, Harbin, 150001, China
* Corresponding Author: Wei-Chiang Hong. Email: email
(This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.073615

Received 22 September 2025; Accepted 17 November 2025; Published online 05 December 2025

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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.