A robotic platform for the synthesis of colloidal nanocrystals

Morphological control with broad tunability is a primary goal for the synthesis of colloidal nanocrystals with unique physicochemical properties. Here we develop a robotic platform as a substitute for trial-and-error synthesis and labour-intensive characterization to achieve this goal. Gold nanocrystals (with strong visible-light absorption) and double-perovskite nanocrystals (with photoluminescence) are selected as typical proof-of-concept nanocrystals for this platform. An initial choice of key synthesis parameters was acquired through data mining of the literature. Automated synthesis and in situ characterization with further ex situ validation was then carried out and controllable synthesis of nanocrystals with the desired morphology was accomplished. To achieve morphology-oriented inverse design, correlations between the morphologies and structure-directing agents are identified by machine-learning models trained on a continuously expanded experimental database. Thus, the developed robotic platform with a data mining-synthesis-inverse design framework is promising in data-driven robotic synthesis of nanocrystals and beyond. Trial-and-error synthesis and labour-intensive characterization procedures hinder the development of nanocrystals. Now, a data-driven robotic synthesis approach is used to prepare gold and double-perovskite nanocrystals. This approach combines data mining of synthesis parameters, robot-assisted synthesis and characterization, and machine-learning-facilitated inverse design of the nanocrystals.