Flow-driven data intensification to accelerate autonomous inorganic materials discovery

Abstract

The rapid discovery of advanced functional materials is critical for overcoming pressing global challenges in energy and sustainability. Despite recent progress in self-driving laboratories and materials acceleration platforms, their capacity to explore complex parameter spaces is hampered by low data throughput. Here we introduce dynamic flow experiments as a data intensification strategy for inorganic materials syntheses within self-driving fluidic laboratories by the continuous mapping of transient reaction conditions to steady-state equivalents. Applied to CdSe colloidal quantum dots, as a testbed, dynamic flow experiments yield at least an order-of-magnitude improvement in data acquisition efficiency and reducing both time and chemical consumption compared to state-of-the-art self-driving fluidic laboratories. By integrating real-time, in situ characterization with microfluidic principles and autonomous experimentation, a dynamic flow experiment fundamentally redefines data utilization in self-driving fluidic laboratories, accelerating the discovery and optimization of emerging materials and creating a sustainable foundation for future autonomous materials research. This study embeds dynamic flow experiments into self-driving laboratories, intensifying data acquisition during autonomous materials synthesis. Demonstrated with colloidal quantum dots, the developed method substantially boosts sampling density over tenfold and reduces time and reagents.