Exploring device physics of perovskite solar cell via machine learning with limited samples

Perovskite solar cells (PSCs) have developed tremendously over the past decade. However, the key factors influencing the power conversion efficiency (PCE) of PSCs remain incompletely understood, due to the complexity and coupling of these structural and compositional parameters. In this research, we demonstrate an effective approach to optimize PSCs performance via machine learning (ML). To address challenges posed by limited samples, we propose a feature mask (FM) method, which augments training samples through feature transformation rather than synthetic data. Using this approach, squeeze-and-excitation residual network (SEResNet) model achieves an accuracy with a root-mean-square-error (RMSE) of 0.833% and a Pearson's correlation coefficient (r) of 0.980. Furthermore, we employ the permutation importance (PI) algorithm to investigate key features for PCE. Subsequently, we predict PCE through high-throughput screenings, in which we study the relationship between PCE and chemical compositions. After that, we conduct experiments to validate the consistency between predicted results by ML and experimental results. In this work, ML demonstrates the capability to predict device performance, extract key parameters from complex systems, and accelerate the transition from laboratory findings to commercial applications.