First-principles and machine learning investigation on A4BX6 halide perovskites

The A₄BX₆ molecular halide perovskites have received attention owing to their interesting optoelectronic properties at the molecular scale; however, a comprehensive dataset of their atomic structures and electronic properties and associated data-driven investigation are still unavailable now, which makes it difficult for inverse materials design for semiconductor applications (e.g. wide band gap semiconductor). In this manuscript, we employ data-driven methods to predict band gaps of A₄BX₆ molecular halide perovskites via machine learning. A large virtual design database including 246 904 A₄BX₆ perovskite samples is predicted via machine learning, based on the model trained using 2740 first-principles results of A₄BX₆ molecular halide perovskites. In addition, symbolic regression-based machine learning is employed to identify more physically intuitive descriptors based on the starting first-principles dataset of A₄BX₆ molecular halide perovskites. In addition, different ranking methods are employed to offer a comprehensive feature importance analysis for the halide perovskite materials. This study highlights the efficacy of machine learning-assisted compositional design of A₄BX₆ perovskites, and the multi-dimensional database established here is valuable for future experimental validation toward perovskite-based wide band gap semiconductor materials.