High-Throughput Study of Antisolvents on the Stability of Multicomponent Metal Halide Perovskites through Robotics-Based Synthesis and Machine Learning Approaches.

Antisolvent crystallization methods are frequently used to fabricate high-quality metal halide perovskite (MHP) thin films, to produce sizable single crystals, and to synthesize nanoparticles at room temperature. However, a systematic exploration of the effect of specific antisolvents on the intrinsic stability of multicomponent MHPs has yet to be demonstrated. Here, we develop a high-throughput experimental workflow that incorporates chemical robotic synthesis, automated characterization, and machine learning techniques to explore how the choice of antisolvent affects the intrinsic stability of binary MHP systems in ambient conditions over time. Different combinations of the end-members, MAPbI3, MAPbBr3, FAPbI3, FAPbBr3, CsPbI3, and CsPbBr3 (MA, methylammonium; FA+, formamidinium), are used to synthesize 15 combinatorial libraries, each with 96 unique combinations. In total, roughly 1100 different compositions are synthesized. Each library is fabricated twice by using two different antisolvents: toluene and chloroform. Once synthesized, photoluminescence spectroscopy is automatically performed every 5 min for approximately 6 h. Nonnegative matrix factorization (NMF) is then utilized to map the time- and compositional-dependent optoelectronic properties. Through the utilization of this workflow for each library, we demonstrate that the selection of antisolvent is critical to the intrinsic stability of MHPs in ambient conditions. We explore possible dynamical processes, such as halide segregation, responsible for either the stability or eventual degradation as caused by the choice of antisolvent. Overall, this high-throughput study demonstrates the vital role that antisolvents play in the synthesis of high-quality multicomponent MHP systems.