Chemical Robotics Enabled Exploration of Stability and Photoluminescent Behavior in Multicomponent Hybrid Perovskites via Machine Learning

Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for the next-generation solar cells; however, issues regarding long-term stability still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: MA_xFA_yCs_1-x-yPbBr₃, MA_xFA_yCs_1-x-yPbI₃, (Cs_xFA_yMA_1-x-yPb(Br_x+yI_1-x-y)₃) and (Cs_xMA_yFA_1-x-yPb(I_x+yBr_1-x-y)₃). We also develop a machine learning-based workflow to quantify the evolution of each system as a function of composition based on overall changes in photoluminescence spectra, as well as specific peak positions and intensities. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.