Bridging Accelerated Indoor Aging and Outdoor Stability of Perovskite Solar Cells Using a Bayesian Modeling Framework

The commercial viability of promising perovskite photovoltaic technologies hinges on their ability to achieve multidecade operational lifetimes, driving a global effort to design accelerated aging tests that can reliably predict real-world stability. However, establishing a link between indoor and outdoor degradation remains challenging, as it typically requires sophisticated characterization techniques that are difficult to implement and interpret. In this work, we demonstrate how coupling physics-based modeling with a probabilistic Bayesian framework allows us to validate the relationship between indoor and outdoor degradation pathways of perovskite solar cells (PSCs) using readily available current–voltage curve data. Our findings reveal that bulk trap density is a dominant degradation mechanism common to p-i-n PSCs tested under various indoor and outdoor conditions, while new degradation modes not yet observed during outdoor exposure emerge under elevated stress levels. Furthermore, they emphasize the need to move beyond efficiency-based lifetime metrics toward a mechanistic framework that can uncover potential failure points. This flexible approach can guide the design of predictive accelerated testing protocols while offering broad applications for optimizing fabrication processes and assessing performance across the solar industry and beyond.