Nanoparticle synthesis assisted by machine learning

Machine learning can be applied for the controlled synthesis of nanoparticles with precise properties. This Review discusses different machine learning approaches for the synthesis of semiconductor, metal, carbon-based and polymeric nanoparticles, and highlights key approaches for the collection of large datasets. Many properties of nanoparticles are governed by their shape, size, polydispersity and surface chemistry. To apply nanoparticles in chemical sensing, medical diagnostics, catalysis, thermoelectrics, photovoltaics or pharmaceutics, they have to be synthesized with precisely controlled characteristics. This is a time-consuming, laborious and resource-intensive task, because nanoparticle syntheses often include multiple reagents and are conducted under interdependent experimental conditions. Machine learning (ML) offers a promising tool for the accelerated development of efficient protocols for nanoparticle synthesis and, potentially, for the synthesis of new types of nanoparticles. In this Review, we discuss ML algorithms that can be used for nanoparticle synthesis and highlight key approaches for the collection of large datasets. We examine ML-guided synthesis of semiconductor, metal, carbon-based and polymeric nanoparticles, and conclude with a discussion of current limitations, advantages and perspectives in the development of ML-assisted nanoparticle synthesis.

Automated summary

This review discusses the use of machine learning in the synthesis of nanoparticles and the importance of data collection methods and nanoparticle properties for their applications.