Data-Driven Exploration of Silver Nanoplate Formation in Multidimensional Chemical Design Spaces

We present an autonomous data-driven framework that iteratively explores the experimental design space of silver nanoparticle synthesis to obtain control over the formation of a desired morphology and size. The objective of the method is to autonomously identify design rules such as the effects of the design variables on the structure of the nanoparticle. The framework balances multimodal characterization methods (i.e. UV-Vis spectroscopy, SAXS, TEM), taking into account the cost of performing a measurement and the quality of information gained. By integrating with an AI agent, we identify important design variables in the synthesis of plate-like silver particles and outline how each variable affects plate thickness, radius, and polydispersity. Our findings are consistent with the literature, demonstrating that the automation framework could be further applied to new systems that have not been well characterized and understood. The framework is generalizable and allows tangible knowledge extraction from the high-throughput experimental runs while still taking inherent stochasticity into consideration.