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.

翻译：利用众多合成变量优化纳米材料合成通常被认为是极其繁琐的工作，因为传统的组合探索方法成本高昂而难以实施。本研究报道了一个自主实验平台，该平台旨在用于具有目标光学特性的纳米粒子（NPs）的定制设计。该平台基于人工智能优化模型的反馈，在NPs的批量合成模块与紫外-可见光谱模块之间以闭环方式运行。以银（Ag）NPs作为代表性示例，我们证明了采用早停准则的贝叶斯优化器能够在仅200次迭代内（在优化五种合成试剂时）高效地制备出精确具有所需吸收光谱的AgNPs。除了卓越的材料开发效率外，对合成变量的分析还揭示了一种涉及柠檬酸盐在AgNP合成中作用的新型化学机制。柠檬酸盐的量是控制球形与片状NPs之间竞争的关键，进而也影响了吸收光谱的形状。我们的研究突显了该平台在提升搜索效率以及通过分析自主实验积累的数据集来提供新型化学知识两方面的能力。