1. Enhancing Power Conversion Efficiency of Perovskite Solar Cells Through Machine Learning Guided Experimental Strategies.
- Author
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Yang, Antai, Sun, Yonggui, Zhang, Jingzi, Wang, Fei, Zhong, Chengquan, Yang, Chen, Hu, Hanlin, Liu, Jiakai, and Lin, Xi
- Subjects
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MACHINE learning , *SOLAR cell efficiency , *SOLAR cells , *PRODUCTION sharing contracts (Oil & gas) , *PEROVSKITE - Abstract
Predicting the power conversion efficiency (PCE) using machine learning (ML) can effectively accelerate the experimental process of perovskite solar cells (PSCs). In this study, a high‐quality dataset containing 2079 experimental PSCs is established to predict PCE values using an accurate ML model, achieving an impressive coefficient of determination (
R 2) value of 0.76. In the 12 validation experiments with PSCs, the average absolute error between the observed and predicted PCE values is only 1.6%. Leveraging the recommended improvement solutions from the ML model, the device's PCE to 25.01% in experimental PSCs is successfully enhanced, thus truly realizing the objective of machine learning‐guided experiments. In addition, by improving the PCE of specific devices, the predicted value can reach 28.19%. The ML model has provided feasible strategies for experimentally improving the PCE of PSCs, which play a crucial role in achieving PCE breakthroughs. [ABSTRACT FROM AUTHOR]- Published
- 2024
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