Machine Learning Prediction of Perovskite Solar Cell Properties Under High Pressure

Halide perovskites are promising solar cell materials due to their suitable bandgap range and high tunability. However, materials based on the organic-inorganic (MA)PbI₃ (MA = CH₃NH₃⁺) suffer a chemical instability issue to heat and moisture due to the volatile MA cation, while the all-inorganic Cs-based analogs present a phase instability challenge where the functional perovskite phases are unstable at ambient conditions and spontaneously convert into the thermodynamically stable non-perovskite phase. Therefore, stabilizing the perovskite phases at the room condition is crucial to achieving higher efficiency and commercialization. Tuning the structure by applying pressure and strain is an effective way to modify the stability and electrical properties of perovskite phases. In this work, we investigate the leading structural features that determine the material properties of the perovskites upon compression. We use various machine learning models to train the large-scale dataset obtained from first-principles DFT calculations. This study will provide insights into developing general models to predict the relationship between structural and electrical properties of similar perovskite structures using cost-effective machine learning approaches.