Machine learning (ML)-assisted optimization doping of KI in MAPbI3solar cells

Perovskite solar cells have drawn extensive attention in the photovoltaic (PV) field due to their rapidly increasing efficiency. Recently, additives have become necessary for the fabrication of highly efficient perovskite solar cells (PSCs). Additionally, alkali metal doping has been an effective method to decrease the defect density in the perovskite film. However, the traditional trial-and-error method to find the optimal doping concentration is time-consuming and needs a significant amount of raw materials. In this work, in order to explore new ways of facilitating the process of finding the optimal doping concentration in perovskite solar cells, we applied a machine learning (ML) approach to assist the optimization of KI doping in MAPbI(3)solar cells. With the aid of ML technique, we quickly found that 3% KI doping could further improve the efficiency of MAPbI(3)solar cells. As a result, a highest efficiency of 20.91% has been obtained for MAPbI(3)solar cells.

Automated summary

Perovskite solar cells have been attracting attention in the PV field for their increasing efficiency. In this study, a machine learning approach was applied to optimize KI doping in MAPbI(3)solar cells, leading to an improved efficiency of 20.91%.