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

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

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Metal halide perovskite (MHP) nanocrystals (NCs) offer extraordinary tunability in their optical properties, yet fully exploiting this potential is challenged by a vast and complex synthesis parameter space. Herein, we introduce Rainbow, a multi-robot self-driving laboratory that integrates automated NC synthesis, real-time characterization, and machine learning (ML)-driven decision-making to efficiently navigate MHP NCs’ mixed-variable high-dimensional landscape. Using parallelized, miniaturized batch reactors, robotic sample handling, and continuous spectroscopic feedback, Rainbow autonomously optimizes MHP NC optical performance—including photoluminescence quantum yield and emission linewidth at a targeted emission energy—through closed-loop experimentation. By systematically exploring varying ligand structures and precursor conditions, Rainbow elucidates critical structure–property relationships and identifies scalable Pareto-optimal formulations for targeted spectral outputs. Rainbow provides a versatile blueprint for accelerated, data-driven discovery and retrosynthesis of high-performance metal halide perovskite nanocrystals, facilitating the on-demand realization of next-generation photonic materials and technologies.

Date: 2025
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DOI: 10.1038/s41467-025-63209-4

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