Machine Learning-Assisted Analysis of Perovskite Solar Cell Long-Term Stability under Multiple Environmental Factors

Perovskite solar cells (PSCs) have made significant strides in the past decade. However, poor long-term stability is a major challenge for PSCs, which hinders large-scale commercialization. Traditional trial-and-error methods are limited by the complexity of the environmental conditions and device structures. This study introduces a machine learning (ML)-assisted approach to analyze factors affecting the PSC stability. A multihead attention mechanism is used to simultaneously process diverse input data, including external and internal parameters. Combined with a squeeze-and-excitation residual network (SEResNet), this approach achieves a coefficient of determination (R2) of 0.972 and a Pearson correlation coefficient (r) of 0.986. Furthermore, the SHapley Additive exPlanations (SHAP) algorithm identifies key factors influencing stability. Through high-throughput prediction of approximately 2000 PSCs, we explore the interactive effects of key factors, offering a comprehensive understanding of their influences on device stability. In addition, we also present the predicted optimal PSC system structure for stability at 85 °C and 85% relative humidity (RH). Subsequently, we conduct device lifetime experiments to present the consistency between experiment and predication results. Hence, this work demonstrates the potential of ML in terms of predicting the stability of PSCs and obtaining critical parameters, facilitating the translation of laboratory research findings into practical applications.