Bespoke Nanoparticle Synthesis and Chemical Knowledge Discovery Via Autonomous Experimentations

TL;DR

This paper presents an autonomous platform for nanoparticle synthesis optimization that efficiently designs nanoparticles with targeted optical properties and uncovers new chemical insights, reducing experimental effort significantly.

Contribution

The work introduces a closed-loop autonomous experimentation platform that combines AI optimization with chemical analysis, enabling rapid nanoparticle design and discovery of novel synthesis chemistry.

Findings

Efficiently optimized silver nanoparticles with desired spectra in 200 iterations

Revealed citrate's role in controlling nanoparticle shape and optical properties

Demonstrated platform's ability to generate new chemical knowledge

Abstract

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.