Enhancing Power Conversion Efficiency of Perovskite Solar Cells Through Machine Learning Guided Experimental Strategies

Abstract

Abstract Predicting the power conversion efficiency (PCE) using machine learning (ML) can effectively accelerate the experimental process of perovskite solar cells (PSCs). In this study, a high‐quality dataset containing 2079 experimental PSCs is established to predict PCE values using an accurate ML model, achieving an impressive coefficient of determination ( R 2 ) value of 0.76. In the 12 validation experiments with PSCs, the average absolute error between the observed and predicted PCE values is only 1.6%. Leveraging the recommended improvement solutions from the ML model, the device's PCE to 25.01% in experimental PSCs is successfully enhanced, thus truly realizing the objective of machine learning‐guided experiments. In addition, by improving the PCE of specific devices, the predicted value can reach 28.19%. The ML model has provided feasible strategies for experimentally improving the PCE of PSCs, which play a crucial role in achieving PCE breakthroughs.