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Comparative benchmark of machine learning models for predicting perovskite solar cell performance
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DOI：10.1016/j.solener.2026.114959.png)
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Abstract
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• A reproducible workflow is generated that serves as a starting point for other authors to guide the fabrication of more efficient devices. • Predictions with high precision values are presented, demonstrating machine learning potential to accelerate the development of PSCs. • A comparison of different machine learning and deep learning models is presented to determine which one best captures the problem.
Keywords:
Perovskite solar cell
SCAPS-1D
Data-driven modeling
Machine learning
Random forest
Synthetic dataset
Multi-output regression
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