Advanced High-Throughput Rational Design of Porphyrin-Sensitized Solar Cells Using Interpretable Machine Learning

Jian Ming Liao, Yu Hsuan Chen, Hsuan Wei Lee, Bo Cheng Guo, Po Cheng Su, Lun Hong Wang, Nagannagari Masi Reddy, Aswani Yella, Zhao Jie Zhang, Chuan Yung Chang, Chia Yuan Chen, Shaik M. Zakeeruddin, Hui Hsu Gavin Tsai, Chen Yu Yeh, Michael Grätzel

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

Accurately predicting the power conversion efficiency (PCE) in dye-sensitized solar cells (DSSCs) represents a crucial challenge, one that is pivotal for the high throughput rational design and screening of promising dye sensitizers. This study presents precise, predictive, and interpretable machine learning (ML) models specifically designed for Zn-porphyrin-sensitized solar cells. The model leverages theoretically computable, effective, and reusable molecular descriptors (MDs) to address this challenge. The models achieve excellent performance on a “blind test” of 17 newly designed cells, with a mean absolute error (MAE) of 1.02%. Notably, 10 dyes are predicted within a 1% error margin. These results validate the ML models and their importance in exploring uncharted chemical spaces of Zn-porphyrins. SHAP analysis identifies crucial MDs that align well with experimental observations, providing valuable chemical guidelines for the rational design of dyes in DSSCs. These predictive ML models enable efficient in silico screening, significantly reducing analysis time for photovoltaic cells. Promising Zn-porphyrin-based dyes with exceptional PCE are identified, facilitating high-throughput virtual screening. The prediction tool is publicly accessible at https://ai-meta.chem.ncu.edu.tw/dsc-meta.

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文章編號2407235
期刊Advanced Science
11
發行號43
DOIs
出版狀態已出版 - 20 11月 2024

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