Intelligent Flow Chemistry: A Data-Driven Approach to Autonomous Inorganic Synthesis

Abstract

The accelerated discovery of advanced functional materials is essential to addressing urgent global challenges related to energy and sustainability. Although self-driving laboratories (SDLs) and materials acceleration platforms (MAPs) have made notable advances, their ability to navigate complex synthesis spaces remains constrained by limited data throughput. In this work, we present Dynamic Flow Experiments (DFEs) as a data intensification approach implemented within Self-Driving Fluidic Laboratories (SDFLs), enabling the continuous translation of transient reaction conditions into their steady-state counterparts. Using CdSe colloidal quantum dots as a model system, DFEs demonstrated a significant enhancement—exceeding an order of magnitude—in data acquisition efficiency, while also minimizing time and reagent usage compared to conventional SDFL methods. Through the integration of in-situ, real-time optical characterization, microscale flow chemistry, and autonomous control, DFEs redefine how data is leveraged in SDFLs, substantially accelerating material discovery and optimization while advancing the sustainability of autonomous research frameworks.