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- Title
Efficient screening framework for organic solar cells with deep learning and ensemble learning.
- Authors
Wang, Hongshuai; Feng, Jie; Dong, Zhihao; Jin, Lujie; Li, Miaomiao; Yuan, Jianyu; Li, Youyong
- Abstract
Organic photovoltaics have attracted worldwide interest due to their unique advantages in developing low-cost, lightweight, and flexible power sources. Functional molecular design and synthesis have been put forward to accelerate the discovery of ideal organic semiconductors. However, it is extremely expensive to conduct experimental screening of the wide organic compound space. Here we develop a framework by combining a deep learning model (graph neural network) and an ensemble learning model (Light Gradient Boosting Machine), which enables rapid and accurate screening of organic photovoltaic molecules. This framework establishes the relationship between molecular structure, molecular properties, and device efficiency. Our framework evaluates the chemical structure of the organic photovoltaic molecules directly and accurately. Since it does not involve density functional theory calculations, it makes fast predictions. The reliability of our framework is verified with data from previous reports and our newly synthesized organic molecules. Our work provides an efficient method for developing new organic optoelectronic materials.
- Subjects
DEEP learning; SOLAR cells; ORGANIC semiconductors; DENSITY functional theory; MOLECULAR structure; CHEMICAL structure; BUILDING-integrated photovoltaic systems
- Publication
NPJ Computational Materials, 2023, Vol 9, Issue 1, p1
- ISSN
2057-3960
- Publication type
Article
- DOI
10.1038/s41524-023-01155-9