Machine‐Learning Modeling for Ultra‐Stable High‐Efficiency Perovskite Solar Cells

Abstract Understanding the key factor driving the efficiency and stability of semiconductor devices is vital. To date, the key factor influencing the long‐term stability of perovskite solar cells (PSCs) remains unknown because of the many influencing factors. In this work, through machine learning, the influences of five factors, including grain size, defect density, bandgap, fluorescence lifetime, and surface roughness, on the efficiency and stability of PSCs have been revealed. It is found that the bandgap has the greatest influence on the efficiency, and the surface roughness and grain size are most influential to the long‐term stability. A mathematical model is given to predict efficiency based on fluorescence lifetime and bandgap. Guided by the model, four groups of experiments are conducted to confirm the machine‐learning predictions and a PSC with 23.4% efficiency and excellent long‐term stability is obtained, as manifested by retention of 97.6% of the initial efficiency after 3288 h aging in the ambient environment, the best stability under these conditions. This work shows that machine learning is an effective means to enrich semiconductor physical models.