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.
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The authors are grateful to the Natural Sciences and Engineering Research Council of Canada (NSERC) via the Discovery Grants program for financial support. A.A.-G., M.A. and T.C.W. acknowledge support from the Office of Naval Research, as well as Tata Sons, Limited. E.K. thanks the Canada Research Chairs Program. A.A.-G. is thankful for the Canada 150 Research Chairs Program. H.T. acknowledges the Connaught International Scholarship for Doctoral Students.
The authors declare no competing interests.
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Tao, H., Wu, T., Aldeghi, M. et al. Nanoparticle synthesis assisted by machine learning. Nat Rev Mater 6, 701–716 (2021). https://doi.org/10.1038/s41578-021-00337-5
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