High-throughput computations and machine learning for halide perovskite discovery

Halide perovskites are materials of considerable interest for solar cells, photodiodes, LEDs, photocatalysis, and photorechargeable batteries. One of the most attractive features of this class of materials is the sheer tunability of their stability, electronic bandgaps, optical absorption behavior, and defect properties, via composition engineering, phase transformation, change in dimensionality, surface and interface engineering, and octahedral rotation and distortion. Due to the ease of simulating well-defined crystal structures and systematically investigating compositional and structural factors that affect their properties, first-principles-based density functional theory (DFT) computations are frequently used for studying halide perovskites, leading to high-throughput data sets, screening of promising materials, and training of machine learning (ML) models for accelerated prediction and optimization. In this article, we present an overview of computational data-driven discovery of novel halide perovskites using some examples from the literature we believe best represent success in this field. Specific methods used for prediction of properties, optimization and screening across large chemical spaces, and automated design of novel structures and compositions, are highlighted. DFT-ML-based design frameworks have been instrumental in expanding the pool of stable halide perovskites with desired optoelectronic properties and will continue to inform new discovery in close synergy with targeted experiments.