Abstract

The optimization of nanomaterial synthesis using numerous synthetic variables is considered to be an extremely laborious task because conventional combinatorial explorations are prohibitively expensive. In this work, an autonomous experimentation platform developed for the bespoke design of metal nanoparticles (NPs) with targeted optical properties is reported. This platform operates in a closed-loop manner between the batch synthesis module of metal NPs and the UV–vis spectroscopy module, based on the feedback of the AI optimization modeling. With silver (Ag) NPs as a representative example, it is demonstrated that the Bayesian optimizer implemented with the early stopping criterion can efficiently produce Ag NPs at room temperature precisely possessing the desired absorption spectra within only 200 iterations (when optimizing among five aqueous synthetic reagents). In addition to the outstanding material developmental efficiency, the analysis of synthetic variables further reveals a novel chemistry involving the quantitative effects of citrate in Ag NP synthesis. The amount of citrate is key to controlling the competition between spherical and plate-shaped NPs and, as a result, affects the shapes of the absorption spectra as well. This study highlights both capabilities of the platform to enhance search efficiencies and to provide novel chemical knowledge by analyzing datasets accumulated from autonomous experimentations.