Analyzing Machine Learning Models to Forecast Hybrid Perovskites’ Dynamic Response to Environmental Stressors

Machine learning (ML) can significantly accelerate the discovery of materials for clean energy. Hybrid perovskites (an emerging class of materials for photovoltaics and light-emitting diodes) encompass an enormous chemical composition space, resulting in a hyperparameter space that is nearly impossible to be interrogated via traditional trial-and-error methods. Thus, there has been a pressing need within the materials research community to identify ML models that can be implemented to inform the physical and chemical behavior of the perovskites. Here, we apply distinct ML models (mostly based on recurrent neural networks) to classify and forecast physical properties encompassing electrical conductivity, photoluminescence response, the power conversion efficiency of photovoltaic devices, etc. Specifically, we use automated, <i>in situ</i> optical measurements to predict the optical response of a series of perovskites for 50+ hours, upon materials’ exposure to moisture. We quantitatively compare linear regression, echo state network, and seasonal auto-regressive integrated moving average with eXogenous regressor algorithms and achieve accuracy of &gt;90% for the latter. Demonstrating the ability of this model to capture non-linear features. Our high-throughput measurements and ML-assisted data analysis exemplifies the potential of ML to diagnose and forecast hybrid perovskites’ response with a variety of chemical compositions.