Efficient Modeling Workflow for Accurate Electronic Structures of Hybrid Perovskites

Hybrid organic-inorganic halide perovskites are the most promising photovoltaic absorber materials to substitute or complement silicon in high-efficiency solar cells. These hybrid materials are often constrained by their low stability and critical elements like lead. Computational high-throughput screening studies, based on solid-state electronic-structure theory, are useful to identify promising substitute materials with targeted properties. In this work, we present an efficient computational approach based on density-functional theory, which is suitable to predict band gaps for arbitrary compounds reliably and in good quantitative agreement with experimental band gap data for known compounds. This approach is described and demonstrated for the building blocks of one of the most promising hybrid perovskites, namely, (HC(NH2)2)xCs1-xPb(IyBr1-y)3, with x and y varied between zero and one.