NanoChef: AI Framework for Simultaneous Optimization of Synthesis Sequences and Reaction Conditions in Autonomous Laboratories

We present NanoChef, a deep learning-based framework for the simultaneous optimization of synthesis sequences and reaction conditions in autonomous laboratories. Unlike traditional AI models focused solely on continuous variables, NanoChef incorporates positional encoding and MatBERT embedding to represent reagent sequences as vectorized inputs. This enables joint modeling of categorical and continuous variables in nanoparticle (NP) synthesis. In virtual experiments, NanoChef consistently identified global optima across synthesis-order-sensitive landscapes, requiring fewer than 40 cycles. For real-world Ag NP synthesis with a λmax of 513 nm by UV‒Vis absorption spectroscopy and high monodispersity, the framework outperformed fixed-order methods, achieving a 32% reduction in the full width at half maximum (FWHM) and reaching optimal recipes within 100 experiments. Extending to a three-reagent system, NanoChef newly discovered an oxidant‒last strategy that yielded the most uniform NPs. This work redefines synthesis order as a tunable design variable and demonstrates how lightweight AI architecture can accelerate autonomous chemistry.