Bespoke Nanoparticle Synthesis and Chemical Knowledge Discovery Via Autonomous Experimentations
This work addresses the problem of inefficient nanomaterial synthesis for materials scientists and chemists, offering a significant improvement in search efficiency and knowledge discovery, though it is incremental as it builds on existing autonomous experimentation methods.
The researchers tackled the laborious optimization of nanoparticle synthesis by developing an autonomous experimentation platform that uses AI-driven feedback to design nanoparticles with targeted optical properties, achieving precise absorption spectra for silver nanoparticles within 200 iterations. This approach also uncovered novel chemical insights, such as the role of citrate in controlling nanoparticle shape and spectral properties.
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.