AI-driven perovskite solar cells optimization

Perovskite solar cells (PSCs) are renowned for their efficiency, affordability, and mass manufacturing. However, the performance unpredictability, material sensitivity and stability issues, and optimization limit their practicality. This study includes the challenges related to PSCs and the role of Artificial Intelligence (AI) in their advancement. AI has shown that it can accelerate the PSC's designs by finding creative solutions. The design assistance provided through AI-based methods reduces the experimentation time and need for resources, enabling real-time production monitoring and control. These methods identify performance bottlenecks and forecast the device efficiency in various settings. In this paper, we have simulated three perovskite solar cell devices (MASnI(3), FASnI(3), and MAGeI(3)) using SCAPS-1D with ETL as ZnO and HTL as Cu2O. Random Forest technique has been used for optimization and prediction of the best PSCs efficiency where the conduction band density of state, thickness of the absorber layer, hole mobility, valence band density of state, and electron mobility have served as design variables. The MSE and R-2 scores for performance prediction are 1.37 x 10(-3) and 0.992 for MASnI(3), 4.21 x 10(-3) and 0.997 for FASnI(3), and 0.79 x 10(-3) and 0.993 for MAGeI(3) respectively.