Data-driven exploration and first-principles analysis of perovskite material

Abstract

In this study, we employ data-driven and first-principles methods (machine learning, density-functional theory and language model) to comprehensively explore crystal structures, electronic properties and applications of an emerging perovskite material, gadolinium scandate (GdScO3), which is an intriguing material that demonstrates potentials in electronics and optics. Using advanced machine learning algorithms based on genetic programming, we have discovered new crystal structures of GdScO3 that have not been previously reported, which are further examined via density functional theory (DFT) calculations and language models to provide detailed insights into their electronic and optical properties and potential applications. Our findings reveal novel new stable phases of GdScO3 and highlight the intricate influence of structural variations on its electronic band structures and light absorption properties. A subsequent domain-specific language model analysis indicates its possibility for photovoltaics pending further efforts to engineer defects revealed in the first-principles calculations. The integration of machine learning with first-principles calculations demonstrates a feasible approach for accelerating the exploration and analysis of materials. This work enriches the understanding of GdScO3 and establishes a robust framework for exploration and ontological analysis of new functional materials combining diverse data-driven techniques (e.g., language model and genetic programming) and first-principles methods.