Self-driving laboratories enable accelerated exploration of chemical and materials spaces by coupling automated experimentation with machine-learning-guided decision making. However, extending autonomous discovery to compositionally complex materials with multiple coupled reaction pathways remains a significant challenge. Here, we introduce PoLARIS, a microfluidic self-driving laboratory designed for time- and material-efficient autonomous synthesis, optimization, and mechanistic interrogation of multi-element nanocrystals. Using PoLARIS, we achieve rapid data-driven optimization of metal halide double perovskite nanoplatelets, comprising up to six distinct elements synthesized via a continuous-flow heat-up reaction. The platform integrates a modular microfluidic reactor architecture with closed-loop experiment selection to efficiently navigate a high-dimensional synthesis parameter space. Beyond autonomous multi-element nanoplatelet synthesis and optimization, PoLARIS utilizes dynamic flow experimentation to enable mechanistic inference of precursor reactivity and reaction pathways governing nanoplatelet formation. This work establishes microfluidic self-driving laboratories as a generalizable approach for unifying autonomous synthesis optimization with mechanistic understanding in compositionally complex colloidal materials systems. PoLARIS framework provides a scalable pathway toward autonomous discovery in other multi-element and high-entropy colloidal nanocrystals beyond double perovskites.
Li et al. (2026) studied this question.