Accelerating the generation and discovery of high-performance donor materials for organic solar cells by deep learning

Sun, Jinyu; Li, Dongxu; Wang, Yue; Xie, Ting; Zou, Yingping; Lu, Hongmei; Zhang, Zhimin

doi:10.1039/d4ta03944k

J. Mater. Chem. A

2024

DOI: 10.1039/d4ta03944k

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Accelerating the generation and discovery of high-performance donor materials for organic solar cells by deep learning

Jinyu Sun,

Dongxu Li,

Yue Wang

et al.

Abstract: Organic solar cells are a potential solution for sustainable energy. However, it is time-consuming and costly to discover high-efficiency materials for organic solar cells. In this study, a deep learning-based...

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Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review

Jiang,

Yao,

Yang

et al. 2024

Solar RRL

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Organic solar cells (OSCs), renowned for their lightweight, cost efficiency, and adaptability nature, stand out as a promising option for developing renewable energy. Improving the power conversion efficiency (PCE) of OSCs is essential, and researchers are delving into novel materials to achieve this. Traditional approaches are often laborious and costly, highlighting the need for predictive modeling. Machine learning (ML), especially via quantitative structure–property relationship (QSPR) models, is streamlining material development, with a goal to exceed a 20% PCE. In this review, the application of ML in OSCs is explored, and recent studies utilizing ML approaches for PCE prediction are reviewed, encompassing empirical functions, ML algorithms, self‐devised ML frameworks, and the combination with automated experimental technologies. First, the benefits of ML in predicting PCE for OSCs are addressed. Second, the development of high‐efficiency predictive models for both fullerene and nonfullerene acceptors is delved into. The impact of various ML algorithm models on PCE prediction is then assessed, taking into account the construction of predictive models. Moreover, the quality of databases and the selection of descriptors are considered. Databases and descriptors based on experimental studies are further categorized. Finally, prospects for the future development of OSCs are proposed.

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Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review

Jiang,

Yao,

Yang

et al. 2024

Solar RRL

View full text Add to dashboard Cite

show abstract

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Accelerating the generation and discovery of high-performance donor materials for organic solar cells by deep learning

Abstract: Organic solar cells are a potential solution for sustainable energy. However, it is time-consuming and costly to discover high-efficiency materials for organic solar cells. In this study, a deep learning-based...

Cited by 1 publication

References 53 publications

Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review

Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review

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