Meet Rainbow: The Multi-Robotic Lab Racing to Uncover the Subsequent Quantum Dots

Researchers at North Carolina State University have unveiled Rainbow, a first-of-its-kind multi-robot self-driving laboratory that autonomously discovers high-performance quantum dots – semiconductor nanoparticles important for next-generation shows, photo voltaic cells, LEDs and quantum-engineering applied sciences. Combining superior robotics with synthetic intelligence, Rainbow can conduct and analyze as much as 1,000 experiments per day with out human intervention, dramatically accelerating the tempo of supplies discovery.

“Rainbow brings together multiple robots working in concert to autonomously explore and optimize complex chemistries with extraordinary efficiency,” says Milad Abolhasani, corresponding writer of a paper on the work and ALCOA Professor of Chemical and Biomolecular Engineering at NC State. “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.”

To use Rainbow, customers start by designating a goal materials property – similar to emission wavelength or bandgap. Users additionally give Rainbow an experimental “budget,” offering it with what number of experiments it ought to conduct earlier than stopping. From there, Rainbow designs, executes and analyzes every experiment, utilizing real-time optical characterization and machine studying to determine what to strive subsequent in its seek for the perfect nanocrystal. In different phrases, it can autonomously decide which quantum dot synthesis recipe will most effectively convert an power enter into the specified power output.

“Rainbow doesn’t sleep; it works around the clock, performing in days what would take human researchers years,” Abolhasani says. “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.”

Abolhasani is a pacesetter in self-driving lab applied sciences, however the robotic components of Rainbow make it a major departure from his earlier work. By utilizing robots to conduct experiments in numerous reactors, Rainbow permits researchers to conduct experiments utilizing a wider vary of precursor chemistries.

“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,” Abolhasani says. “In addition, Rainbow permits us to discover varied ligand buildings on the floor of those nanocrystals, which might play a key position in controlling the properties of those 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,” stated Abolhasani. “That’s the power of combining robotics, AI and chemistry in a single, intelligent lab platform.”

And as soon as Rainbow has recognized the most effective recipe for producing a fascinating quantum dot, the system can then be transformed from working small-scale batch reactors for analysis functions to working large-scale reactors for manufacturing.

“Rainbow makes scaling up a seamless transition,” Abolhasani says.

The paper, “Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals,” is revealed within the journal Nature Communications. First writer of the paper is Jinge Xu, a Ph.D. pupil at NC State. The paper was co-authored by Christopher Moran, Nikolai Mukhin, Pragyan Jha, Fernando Delgado Licona and Sina Sadeghi, Ph.D. college students at NC State; Arup Ghorai, a former postdoctoral researcher at NC State; Fazel Bateni, a former Ph.D. pupil at NC State; Jeffery Bennett, a postdoctoral researcher at NC State; Koray Latif and Andrew Cahn, undergraduates at NC State; and Lior Politi, a former highschool pupil intern at NC State.

This work was accomplished with help from the University of North Carolina Research Opportunities Initiative (UNC ROI) and the National Science Foundation, beneath grants 1940959 and 2208406.

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Note to Editors: The examine summary follows.

“Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals”

Authors: Jinge Xu, Christopher H.J. Moran, Arup Ghorai, Fazel Bateni, Jeffery A. Bennett, Nikolai Mukhin, Koray Latif, Andrew Cahn, Pragyan Jha, Fernando Delgado Licona, Sina Sadeghi, Lior Politi and Milad Abolhasani, North Carolina State University

Published: Aug. 22, Nature Communications

DOI: 10.1038/s41467-025-63209-4

Abstract: Metal halide perovskite (MHP) nanocrystals (NCs) supply extraordinary tunability of their optical properties, but absolutely exploiting this potential is challenged by an enormous and complicated synthesis parameter area. Here, we introduce “Rainbow,” a multi-robot self-driving laboratory that integrates automated NC synthesis, real-time characterization, and machine studying (ML)-driven decision-making to effectively navigate MHP NCs’ mixed-variable high-dimensional panorama. Using parallelized, miniaturized batch reactors, robotic pattern dealing with, and steady spectroscopic suggestions, Rainbow autonomously optimizes MHP NC optical efficiency—together with photoluminescence quantum yield and emission linewidth at a focused emission power—via closed-loop experimentation. By systematically exploring various ligand buildings and precursor circumstances, Rainbow elucidates important construction–property relationships and identifies scalable Pareto-optimal formulations for focused spectral outputs. Rainbow gives a flexible blueprint for accelerated, data-driven discovery and retrosynthesis of high-performance nanomaterials, paving the best way for on-demand realization of next-generation photonic supplies and applied sciences.