Machine learning will revolutionize perovskite solar cells
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REFERENCES
[1] Xu, Y., Wang, F., An, Z., et al. (2023). Artificial intelligence for science—bridging data to wisdom. Innovation 4(6): 100525. https://doi.org/10.1016/j.xinn.2023.100525. [2] Liu, W., Meng, N., Huo, X., et al. (2023). Machine learning enables intelligent screening of interface materials towards minimizing voltage losses for p-i-n type perovskite solar cells. J. Energy Chem. 83(8): 128–137. https://doi.org/10.1016/j.jechem.2023.04.015. [3] Zhi, C., Wang, S., Sun, S., et al. (2023). Machine-Learning-Assisted Screening of Interface Passivation Materials for Perovskite Solar Cells. ACS Energy Lett. 8(3): 1424–1433. https://doi.org/10.1021/acsenergylett.2c02818. [4] Xu, J., Chen, H., Grater, L., et al. (2023). Anion optimization for bifunctional surface passivation in perovskite solar cells. Nat. Mater. 22(12): 1507–1514. https://doi.org/10.1038/s41563-023-01705-y. [5] Jacobsson, T.J., Hultqvist, A., García-Fernández, A., et al. (2021). An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles. Nat. Energy 7(1): 107–115. https://doi.org/10.1038/s41560-021-00941-3. -
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Chen Z., Pan S., Wang J., et al., (2024). Machine learning will revolutionize perovskite solar cells. The Innovation 5(3), 100602. https://doi.org/10.1016/j.xinn.2024.100602
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Chen Z., Pan S., Wang J., et al., (2024). Machine learning will revolutionize perovskite solar cells. The Innovation 5(3), 100602. https://doi.org/10.1016/j.xinn.2024.100602
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Figure 1.
The research process of perovskite solar cells combined with machine learning includes four main parts: data collection, model training, predicting target attributes, and experimental verification
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