Robot-Accelerated Perovskite Investigation and Discovery

Abstract

Metal halide perovskites are a promising class of materials for next-generation photovoltaic and optoelectronic devices. The discovery and full characterization of new perovskite-derived materials are limited by the difficulty of growing high quality crystals needed for single-crystal X-ray diffraction studies. We present an automated, high-throughput approach for metal halide perovskite single crystal discovery based on inverse temperature crystallization (ITC) as a means to rapidly identify and optimize synthesis conditions for the formation of high quality single crystals. Using this automated approach, a total of 8172 metal halide perovskite synthesis reactions were conducted using 45 organic ammonium cations. This robotic screening increased the number of metal halide perovskite materials accessible by an ITC synthesis route by more than 5-fold and resulted in the formation of two new phases, [C2H7N2][PbI3] and [C7H16N]2[PbI4]. This comprehensive data set allows for a statistical quantification of the total experimental space and of the likelihood of large single crystal formation. Moreover, this data set enables the construction and evaluation of machine learning models for predicting crystal formation conditions. This work is a proof-of-concept that combining high throughput experimentation and machine learning accelerates and enhances the study of metal halide perovskite crystallization. This approach is designed to be generalizable to different synthetic routes for the acceleration of materials discovery.