Robot-Accelerated Perovskite Investigation and Discovery

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.