Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review DOI
Yang Jiang, Chuang Yao,

Yezi Yang

et al.

Solar RRL, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 9, 2024

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

Language: Английский

Recent Developments in Organic Photodetectors for Future Industrial Applications: A Review DOI
J. Ajayan,

S. Sreejith,

Ajith Ravindran

et al.

Sensors and Actuators A Physical, Journal Year: 2025, Volume and Issue: unknown, P. 116459 - 116459

Published: March 1, 2025

Language: Английский

Citations

2
Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review DOI
Yang Jiang, Chuang Yao,

Yezi Yang

et al.

Solar RRL, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 9, 2024

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

Language: Английский

Citations

2