Probing the Low-Frequency Response of Impedance Spectroscopy of Halide Perovskite Single Crystals Using Machine Learning.

Electrochemical impedance spectroscopy (EIS) has emerged as a versatile technique for characterization and analysis of metal halide perovskite solar cells (PSCs). The crucial information about ion migration and carrier accumulation in PSCs can be extracted from the low-frequency regime of the EIS spectrum. However, lengthy measurement time at low frequencies along with material degradation due to prolonged exposure to light and bias motivates the use of machine learning (ML) in predicting the low-frequency response. Here, we have developed an ML model to predict the low-frequency response of the halide perovskite single crystals. We first synthesized high-quality MAPbBr