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 a variety of optoelectronic and sensing applications. However, issues regarding long-term stability have emerged as the key bottleneck for applications and 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 have both established a new workflow and found out the main tendencies in the mixed cation and anion systems, which led to the discovery of non-trivial composition regions with high stability. The Non-negative Matrix Factorization and Gaussian Process regression are used to interpolate the photoluminescent behavior of vast compositional space and to study the overall behavior of the phase diagram. This interpolative regression analysis helps to distinguish mixtures that form solid solutions from those that segregate into multiple materials, pointing out the most stable regions of the phase diagram. 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.