Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning

Abstract Nanoparticles play irreplaceable roles in optoelectronic sensing, medical therapy, material science, and chemistry due to their unique properties. There are many synthetic pathways used for the preparation of nanoparticles, and different synthetic pathways can produce nanoparticles with different properties. Therefore, it is crucial to control the properties of nanoparticles precisely to impart the desired functions. In general, the properties of nanoparticles are influenced by their sizes and morphologies. Current technology for the preparation of nanoparticles on microfluidic chips requires repeated experimental debugging and significant resources to synthesize nanoparticles with precisely the desired properties. Machine learning-assisted synthesis of nanoparticles is a sensible choice for addressing this challenge. In this paper, we review many recent studies on syntheses of nanoparticles assisted by machine learning. Moreover, we describe the working steps of machine learning, the main algorithms, and the main ways to obtain datasets. Finally, we discuss the current problems of this research and provide an outlook.