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Machine learning (ML)-assisted optimization doping of KI in MAPbI3 solar cells

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Abstract

Perovskite solar cells have drawn extensive attention in the photovoltaic (PV) field due to their rapidly increasing efficiency. Recently, additives have become necessary for the fabrication of highly efficient perovskite solar cells (PSCs). Additionally, alkali metal doping has been an effective method to decrease the defect density in the perovskite film. However, the traditional trial-and-error method to find the optimal doping concentration is time-consuming and needs a significant amount of raw materials. In this work, in order to explore new ways of facilitating the process of finding the optimal doping concentration in perovskite solar cells, we applied a machine learning (ML) approach to assist the optimization of KI doping in MAPbI3 solar cells. With the aid of ML technique, we quickly found that 3% KI doping could further improve the efficiency of MAPbI3 solar cells. As a result, a highest efficiency of 20.91% has been obtained for MAPbI3 solar cells.

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Acknowledgements

This work was financially supported by Nanchang University High Talent Project (No. 9166-2701010119), the National Key R&D Program of China (No. 2016YFB0401003) and the National Natural Science Foundation of China (Nos.61935016, 61775004 and U1605244).

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Authors and Affiliations

  1. Institute for Advanced Study, Nanchang University, Jiangxi, 330031, China

    Sheng Jiang, Fan Li & Min-Lin Jiang

  2. State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, Department of Physics, Peking University, Beijing, 100871, China

    Cun-Cun Wu, Yu-Qing Zhang, Ze-Hao Zhang, Qiao-Hui Zhang, Zhi-Jian Chen, Bo Qu & Li-Xin Xiao

  3. School of Materials Science and Engineering, Nanchang University, Jiangxi, 330031, China

    Sheng Jiang

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  1. Sheng Jiang
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Correspondence to Li-Xin Xiao or Min-Lin Jiang.

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Jiang, S., Wu, CC., Li, F. et al. Machine learning (ML)-assisted optimization doping of KI in MAPbI3 solar cells. Rare Met. 40, 1698–1707 (2021). https://doi.org/10.1007/s12598-020-01579-y

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