Data-driven analysis of hysteresis and stability in perovskite solar cells using machine learning

Perovskite solar cells are promising photovoltaic devices because of the high defect tolerance and desirable optoelectronic properties of the metal-halide perovskite absorber materials. The transition from lab to industry is still an open problem, which is mainly limited by upscaling and stability. In this study we try to use tools from data science namely Pearson correlation and random forest regressor applied to the data from the open-source platform “Perovskite Database” to understand the correlations with material choice, fabrication techniques, and current-voltage key features to the stability and hysteresis index. We find that the cell stack as a whole plays a crucial role in hysteresis and not a single layer. We statistically confirm that p-i-n and higher-efficient solar cells generally show reduced hysteresis. We identify certain cross correlations, which would lead to wrong conclusions e.g. claiming an open-circuit voltage not correlated with the hysteresis or some apparent correlations with material parameters, which originate from the historical development. Regarding stability, we are not able to obtain good performance from the machine learning model. Reasons are non-standardized measurements and lack of sufficient data.