Accelerating Perovskite Solar Cell Development: A Machine Learning-Driven Framework with SHAP Explainability
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Accelerating Perovskite Solar Cell Development: A Machine Learning-Driven Framework with SHAP Explainability
“In the field of new solar cell technology, experts have proposed an intelligent optimization method for the preparation process of perovskite solar cells based on machine learning, which effectively improves the photoelectric conversion efficiency and provides a new perspective for the development of high-efficiency solar cells.”
Chinese Journal of LuminescencePages: 1-12(2025)
作者机构:
1.五邑大学 电子与信息工程学院,广东 江门 529020
2.暨南大学 物理与光电工程学院, 广东 广州 510632
3.澳门大学 应用物理与材料工程学院, 中国 澳门 999078
作者简介:
基金信息:
the National Natural Science Foundation of China (NSFC) Young Scientist Fund(62405113);Guangzhou Science and Technology Program Project(SL2024A04J00418);The Key Realm R&D Program of Guangdong Province(2019B010132004)
Liang Ruiquan,Liu Qian,Hu Chunhua,et al.Accelerating Perovskite Solar Cell Development: A Machine Learning-Driven Framework with SHAP Explainability[J].Chinese Journal of Luminescence,
Machine Learning Assisted Optimization of Perovskite Thin Film Fabrication Process and Assessment of Feature Importance
Bimolecularly Passivated Buried Interface for Highly Efficient Perovskite Solar Cells
Solvent Regulation Strategy for Ambient Preparation of Perovskite Solar Cells
Multifunctional Orotic Acid Passivation for Efficient and Stable Perovskite Solar Cells
Strategies and Challenges of Data-driven Research and Discovery in Luminescent Materials
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GONG Jian
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LIU Chong
MAI Yaohua
Related Institution
Faculty of Intelligent Manufacturing, Wuyi University
College of Photonic and Electronic Engineering, Fujian Normal University
College of Chemical Engineering and Material, Quanzhou Normal University
College of Physics and Information Engineering, Quanzhou Normal University
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