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               161. Odabaşı, Ç.; Yıldırım, R. Performance analysis of perovskite solar cells in 2013-2018 using machine-learning tools. Nano. Energy. 2019,
                  56, 770-91. DOI
               162. Yang, A.; Sun, Y.; Zhang, J.; et al. Enhancing power conversion efficiency of perovskite solar cells through machine learning guided
                  experimental strategies. Adv. Funct. Mater. 2024, 35, 2410419. DOI
               163. Liu, T.; Evans, N.; Ji, K.; et al. Disentangling environmental effects on perovskite solar cell performance via interpretable machine
                  learning. ACS. Energy. Lett. 2026, 11, 1609-17. DOI
               164. Liao, J. M.; Chen, Y. H.; Lee, H. W.; et al. Advanced high-throughput rational design of porphyrin-sensitized solar cells using
                  interpretable machine learning. Adv. Sci. 2024, 11, 2407235. DOI PubMed PMC
               165. Pu, Y.; Dai, Z.; Zhou, Y.; et al. Data‐driven molecular encoding for efficient screening of organic additives in perovskite solar cells. Adv.
                  Funct. Mater. 2025, 36, e06672. DOI
               166. Sanimu, S. N.; Yang, H. Y.; Kandel, J.; et al. Machine learning-assisted fabrication of PCBM‐perovskite solar cells with nanopatterned
                  TiO 2  layer. Energy. Environ. Mater. 2023, 7, e12676. DOI
               167. Jiang, S.; Wu, C. C.; Li, F.; et al. Machine learning (ML)‐assisted optimization doping of KI in MAPbI 3  solar cells. Rare. Metals. 2020,
                  40, 1698-707. DOI
               168. Miftahullatif, E. B.; Pethe, S. D.; Low, A. K. Y.; et al. Machine-learning-driven in-device optimization of all-printed perovskite solar
                  cells. ACS. Energy. Lett. 2025, 10, 3952-61. DOI
               169. Lou, Q.; Wang, J.; Nie, Z.; et al. Prediction and fine screening of small molecular passivation materials for high-efficiency perovskite
                  solar cells via an enhanced machine learning workflow. Adv. Funct. Mater. 2025, 35, e11549. DOI
               170. Zhang, J.; Wu, J.; Le, Corre. V. M.; Hauch, J. A.; Zhao, Y.; Brabec, C. J. Advancing perovskite photovoltaic technology through
                  machine learning‐driven automation. InfoMat 2025, 7, e70005. DOI
               171. Tao, Q.; Xu, P.; Li, M.; Lu, W. Machine learning for perovskite materials design and discovery. NPJ. Comput. Mater. 2021, 7, 23. DOI
               172. Zhang, J.; Le, Corre. V. M.; Wu, J.; et al. Autonomous optimization of air‐processed perovskite solar cell in a multidimensional
                  parameter space. Adv. Energy. Mater. 2025, 15, 2404957. DOI
               173. Liu, Y.; Tan, X.; Liang, J.; Han, H.; Xiang, P.; Yan, W. Machine learning for perovskite solar cells and component materials: key
                  technologies and prospects. Adv. Funct. Mater. 2023, 33, 2214271. DOI




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