Machine learning (ML)-assisted optimization doping of KI in MAPbI 3 solar 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 MAPbI3 solar cells. With the aid of ML technique, we quickly found that 3% KI doping could further improve the efficiency of MAPbI3 solar cells. As a result, a highest efficiency of 20.91% has been obtained for MAPbI3 solar cells.