Bespoke Nanoparticle Synthesis and Chemical Knowledge Discovery Via
  Autonomous Experimentations
Hyuk Jun Yoo Nayeon Kim Heeseung Lee Daeho Kim Leslie Tiong Ching Ow Hyobin Nam Chansoo Kim Seung Yong Lee Kwan-Young Lee Donghun Kim Sang Soo Han
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.