A Data-Driven Platform for Two-Dimensional Hybrid Lead-Halide Perovskites.

Abstract

The exceptional properties of two-dimensional hybrid organic-inorganic lead-halide perovskites (2D HOIPs) have led to a rapid increase in the number of low-dimensional materials for optoelectronic engineering and solar energy conversion. The flexibility and controllability of 2D HOIPs create a vast structural space, which presents an urgent issue to effectively explore 2D HOIPs with better performance for practical applications. However, the traditional RP-DJ classification method falls short in describing the influence of structure on the electronic properties of 2D HOIPs. To overcome this limitation, we employed inorganic structure factors (SF) as a classification descriptor, which considers the influence of inorganic layer distortion of 2D HOIPs. And we investigated the relationship between SF, other physicochemical features, and band gaps of 2D HOIPs. By using this structural descriptor as a feature for a machine learning model, a database of 304920 2D HOIPs and their structural and electronic properties was generated. A large number of previously neglected 2D HOIPs were discovered. With the establishment of this database, experimental data and machine learning methods were combined to develop a 2D HOIPs exploration platform. This platform integrates searching, download, analysis, and online prediction, providing a useful tool for the further discovery of 2D HOIPs.