Predicting the device performance of the perovskite solar cells from the experimental parameters through machine learning of existing experimental results

The performance of the metal halide perovskite solar cells (PSCs) highly relies on the experimental parameters, including the fabrication processes and the compositions of the perovskites; tremendous experimental work has been done to optimize these factors. However, predicting the device performance of the PSCs from the fabrication parameters before experiments is still challenging. Herein, we bridge this gap by machine learning (ML) based on a dataset including 1072 devices from peer-reviewed publications. The optimized ML model accurately predicts the PCE from the experimental parameters with a root mean square error of 1.28% and a Pearson coefficient r of 0.768. Moreover, the factors governing the device performance are ranked by shapley additive explanations (SHAP), among which, A-site cation is crucial to getting highly efficient PSCs. Experiments and density functional theory calculations are employed to validate and help explain the predicting results by the ML model. Our work reveals the feasibility of ML in predicting the device performance from the experimental parameters before experiments, which enables the reverse experimental design toward highly efficient PSCs.