Screening of Functional Small Molecules via Modified Machine Learning Strategy toward Efficient All-Inorganic Perovskite Solar Cells.

Abstract

Organic small molecules are proven to be capable of passivating the bulk/interfacial defects in inorganic perovskite solar cells. Considering the burdensome situation to screen the functional small molecules, we employ a modified machine learning (ML) strategy to guide screening suitable small molecules toward efficient solar cells through three modified ML algorithms to construct the prediction model: (i) random forest algorithm (RF), (ii) support vector machine algorithm (SVR), and (iii) XGBoost. Among them, the XGBoost algorithm displays a better overall predictive performance, whereby the R2 index reaches 0.939. Accordingly, eight small molecules are selected to modify the interface of perovskite films, and both the theoretical and experimental results certify that the difluorobenzylamine with additional fluorine atoms has a better interface modification effect among the small molecules containing functional groups, e.g., the benzene ring and amino group. The high accuracy of the modified machine learning model enables us to simplify the small-molecule screening process and form an important step for ongoing developments in perovskite solar cells and other optoelectronic devices.