How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells

Characterizing the electrical parameters of perovskite solar cells (PSCs) usually requires a lot of time to fabricate complete devices. Here, machine learning (ML) is used to reduce the device fabrication process and predict the electrical performance of PSCs. Using ML algorithms and 814 valid data cleaned from 2735 peer‐reviewed publications, ML prediction models are built for bandgap, conduction band minimum, valence band maximum of perovskites, and electrical parameters of PSCs. These prediction models have excellent accuracy, and the root mean square error of the prediction models for bandgap and power conversion efficiency (PCE) reaches 0.064 eV and 1.58%, respectively. Among the many factors that affect the performance of PSCs, those factors play a major role in the lack of comprehensive explanation. Through the prediction model of electrical parameters and Shapley Additive explanations theory, the factors affecting the PCE of PSCs are explained and analyzed. It can not only verify the objective physical laws from the perspective of ML, but also conclude that among the 13 features, the content of formamidinium/NH 2 CHNH 2 + plays the most important role in improving the PCE of PSCs. These results show that ML has great application possibilities in the PSC field.