Artificial intelligence high-throughput prediction building dataset to enhance the interpretability of hybrid halide perovskite bandgap

Abstract

The bandgap is a key parameter for understanding and designing hybrid perovskite material properties, as well as developing photovoltaic devices. Traditional bandgap calculation methods like ultraviolet-visible spectroscopy and first-principles calculations are time- and power-consuming, not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space. In the present work, an artificial intelligence ensemble comprising two classifiers (with F1 scores of 0.9125 and 0.925) and a regressor (with mean squared error of 0.0014 eV) is constructed to achieve high-precision prediction of the bandgap. The bandgap perovskite dataset is established through high-throughput prediction of bandgaps by the ensemble. Based on the self-built dataset, partial dependence analysis (PDA) is developed to interpret the bandgap influential mechanism. Meanwhile, an interpretable mathematical model with an R² of 0.8417 is generated using the genetic programming symbolic regression (GPSR) technique. The constructed PDA maps agree well with the Shapley Additive exPlanations, the GPSR model, and experiment verification. Through PDA, we reveal the boundary effect, the bowing effect, and their evolution trends with key descriptors.