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

本研究提出了一种自主实验平台，用于设计具有目标光学特性的纳米颗粒（NPs），并展示了该平台在银（Ag）NPs 合成中的应用，表明该平台能够高效地生成具有所需吸收光谱的 Ag NPs，并进一步揭示了柠檬酸调控竞争将会影响吸收光谱形状的新型化学知识。