The role of flow chemistry in self-driving labs

Self-driving laboratories (SDLs) are transforming chemical and materials discovery by integrating high-throughput experimentation with artificial intelligence (AI)-driven decision-making. The success of SDLs hinges on their ability to generate and analyze high-quality, high-density experimental data, enabling rapid optimization and autonomous exploration of complex chemical spaces. In this perspective, we examine how flow chemistry serves as a foundational platform for SDLs by offering continuous synthesis, real-time analytics, and modular configurations that maximize data acquisition and enhance flexibility. The synergy between miniaturization, continuous reaction monitoring, and adaptive control establishes a framework for scalable, data-rich experimentation within SDLs. Additionally, we discuss emerging approaches to digitalizing the design of fluidic robots by codifying reactor engineering principles, enabling automated configuration for diverse chemistries. As AI-driven planning evolves, SDLs with autonomous flow capabilities will accelerate discovery and establish a new paradigm in intelligent, data-driven materials science research.