Two-step machine learning enables optimized nanoparticle synthesis

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Two-step machine learning enables optimized nanoparticle synthesis
Title:
Two-step machine learning enables optimized nanoparticle synthesis
Other Titles:
npj Computational Materials
Publication Date:
20 April 2021
Citation:
Mekki-Berrada, F., Ren, Z., Huang, T. et al. Two-step machine learning enables optimized nanoparticle synthesis. npj Computational Materials (2021). http://doi.org/10.1038/s41524-021-00520-w
Abstract:
AbstractIn materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Fund
Grant Reference no. : A1898b0043

This research / project is supported by the National Research Foundation (NRF) / Singapore MIT Alliance for Research and Technology’ (SMART) - Campus for Research Excellence and Technological Enterprise (CREATE) / Low energy electronic systems (LEES)
Grant Reference no. : NA
Description:
ISSN:
2057-3960