These semiconductor nanoparticles are essential to next-generation technologies, including advanced displays, solar cells, LEDs, and quantum-engineered systems. By merging robotics with artificial intelligence, Rainbow can run and analyze up to 1000 experiments per day without human input, significantly speeding up the process of materials discovery.
Rainbow brings together multiple robots working in concert to autonomously explore and optimize complex chemistries with extraordinary efficiency. Rainbow’s robots automatically prepare chemical precursors, mix them, and execute multiple reactions in parallel using miniaturized batch reactors – up to 96 reactions at a time. The system then automatically transfers all reaction products to a characterization robot, which analyzes the outcomes. From start to finish, every step is fully automated and intelligently coordinated.
Milad Abolhasani, Study Corresponding Author and ALCOA Professor, Chemical and Biomolecular Engineering, North Carolina State University
Here’s how it works: Rainbow’s robotic system automatically prepares chemical precursors, mixes them, and performs multiple reactions in parallel using miniaturized batch reactors—up to 96 at once. The resulting products are then transferred to a characterization robot, which analyzes the outcomes. From start to finish, the entire process is fully automated and intelligently managed.
To get started, users simply specify a target material property, such as emission wavelength or bandgap, and set an experimental “budget,” or the number of experiments to run. Rainbow takes it from there, using real-time optical characterization and machine learning to determine the most promising next steps. In short, it autonomously figures out which quantum dot synthesis recipe is most likely to produce the desired result with optimal efficiency.
“Rainbow doesn’t sleep; it works around the clock, performing in days what would take human researchers years. But it’s not designed to replace scientists; it’s built to empower them by handling the tedious, time-intensive parts of discovery so they can focus on design and innovation,” stated Abolhasani.
While Abolhasani has been a key figure in the development of self-driving lab technologies, Rainbow represents a major step forward. Unlike earlier systems, it uses multiple robots to run experiments across different reactors, enabling a broader exploration of chemical precursors.
Abolhasani further added, “Because we are not confined to a fixed set of precursors, there is a wider range of potential outcomes in terms of what the highest quality quantum dot will be made of. In addition, Rainbow allows us to explore various ligand structures on the surface of these nanocrystals, which can play a key role in controlling the properties of these quantum dots."
“With Rainbow, we’ve built a system that not only finds the best quantum dots faster than ever before, it also tells us why they work. That’s the power of combining robotics, AI, and chemistry in a single, intelligent lab platform,” Abolhasani noted.
Beyond research, Rainbow can also scale up. Once it finds an optimal recipe, the system can transition from small-scale batch reactors to larger-scale manufacturing reactors without a hitch.
“Rainbow makes scaling up a seamless transition,” stated Abolhasani.
Journal Reference:
Xu, J., et al. (2025) Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals. Nature Communications. doi.org/10.1038/s41467-025-63209-4.