A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles

Abstract

Traditional nanomaterial development faces inefficiency and unstable results due to labor-intensive trial-and-error methods. To overcome these challenges, we developed a data-driven automated platform integrating artificial intelligence (AI) decision modules with automated experiments. Specifically, the platform employs a Generative Pre-trained Transformer (GPT) model to retrieve methods/parameters and implements an A* algorithm centered closed-loop optimization process. It achieves optimized diverse nanomaterials (Au, Ag, Cu2O, PdCu) with controlled types, morphologies, and sizes, demonstrating efficiency and repeatability. Using the A* algorithm, we comprehensively optimized synthesis parameters for multi-target Au nanorods (Au NRs) with longitudinal surface plasmon resonance (LSPR) peak under 600-900 nm across 735 experiments, and for Au nanospheres (Au NSs)/Ag nanocubes (Ag NCs) in 50 experiments. Reproducibility tests showed deviations in characteristic LSPR peak and full width at half maxima (FWHM) of Au NRs under identical parameters were ≤1.1 nm and ≤ 2.9 nm, respectively. Researchers only need initial script editing and parameter input, significantly reducing human resource requirements. Comparative analysis confirms the A* algorithm outperforms Optuna and Olympus in search efficiency, requiring significantly fewer iterations. Traditional nanoparticle synthesis faces inefficiency issues. Here, authors developed an AI-driven robotic platform combining GPT models and A algorithm to autonomously optimize metallic nanoparticle synthesis with minimal experiments, achieving high reproducibility.