Chemical library generation of polymer acceptors for organic solar cells with higher electron affinity

Alzahrani, Fatimah Mohammed A.; Naeem, Sumaira; Khan, Numan; Siddique, Bilal; Faizan Nazar, Muhammad; Kadyrov, Tagir; Alrowaili, Z.A.; Al-Buriahi, M.S.

doi:10.1016/j.commatsci.2024.112984

Computational Materials Science

2024

DOI: 10.1016/j.commatsci.2024.112984

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Chemical library generation of polymer acceptors for organic solar cells with higher electron affinity

Fatimah Mohammed A. Alzahrani,

Sumaira Naeem,

Numan Khan

et al.

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2024

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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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Chemical library generation of polymer acceptors for organic solar cells with higher electron affinity

Cited by 1 publication

References 60 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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