"Rainbow brings together multiple robots working in concert to autonomously explore and optimize complex chemistries with extraordinary efficiency," said Milad Abolhasani, ALCOA Professor of Chemical and Biomolecular Engineering at NC State and corresponding author of the study.

The platform enables users to define a target property, such as emission wavelength or bandgap, along with an experimental budget. Rainbow then designs, executes, and analyzes each experiment, relying on real-time optical characterization and machine learning to refine its search for the best nanocrystal recipe.

"Rainbow doesn't sleep; it works around the clock, performing in days what would take human researchers years," Abolhasani said. "It's not designed to replace scientists but to empower them by removing repetitive, time-consuming tasks."

Unlike earlier self-driving labs, Rainbow uses multiple robots to perform reactions across different reactors, allowing exploration of a broader set of precursor chemistries and ligand structures. This flexibility enhances the ability to identify why certain quantum dots perform well, not just which ones do.

Once Rainbow identifies the most effective synthesis pathway, it can scale up seamlessly from miniature batch reactors to large-scale production, bridging the gap between laboratory discovery and industrial manufacturing.

Research Report:Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals

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