Halide perovskites (HPs) are a novel materials class that has attracted tremendous attention from the photovoltaic (PV) community. Since their discovery in 2012, the light-to-energy conversion efficienct of perovsktie solar cells (PSCs) rapidly rose from 10.0% to 25.5%. It has overtaken the common inorganic-based thin-film techniques such as CdTs and CIGS, and is approaching the most conventional crystalline-Si devices. HPs are promising not only for emergent solar cells but also for indoor PVs and electroluminescence (light-emitting diodes). Apart from their high efficiencies, they also take advantage of their low production cost and composition flexibility.

We are modelling both hybrid (organic-inorganic) and all-inorganic HPs as well as HP-based model systems to understand the materials structure and properties and fundamental processes in optoelectronic application. We use density-functional theory (DFT), DFT-based atomistic thermodynamics, and DFT-based machine learning methods to investigate the atomic structure, crystal structure, structural variation, electronic structure, and charge-transport dynamics in the bulk, at the surfaces, and at the interfaces. Ultimately, we aim to support and direct the design of next-generation HP materials for highly-efficient conversion zwischen light energy and electric power.