Machine learning for carbon dot synthesis and applications

Abstract

One of the hottest topics in nanoparticles research right now is carbon dots (CDs). In order to be used in applications like medical imaging and diagnostics, pharmaceutics, optoelectronics, and photocatalysis, CDs must be synthesized with carefully controlled properties. This is often a tedious task due to the fact that nanoparticle syntheses frequently involve multiple chemicals and are carried out under complex experimental conditions. The emerging data-driven methods from artificial intelligence (AI) and machine learning (ML) provide promising tools to go beyond the time-consuming and laborious trial-and-error approach. In this review, we focus on the recent uses of ML accelerating exploration of the CD chemical space. Future applications of these methods address the current limitations in CD synthesis expanding the potential uses of these intriguing nanoparticles.