Two-step machine learning enables optimized nanoparticle synthesis
Mekki-Berrada, Flore Ren, Zekun Huang, Tan Wong, Wai Kuan Zheng, Fang Xie, Jiaxun Tian, Isaac Parker Siyu Jayavelu, Senthilnath Mahfoud, Zackaria Bash, Daniil
Ren, Zekun
Tian, Isaac Parker Siyu
Jayavelu, Senthilnath
Mahfoud, Zackaria
Bash, Daniil
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Abstract
In 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. © 2021, The Author(s).
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Source Title
npj Computational Materials
Publisher
Nature Research
Series/Report No.
Collections
Rights
Attribution 4.0 International
Date
2021-04-20
DOI
10.1038/s41524-021-00520-w
Type
Article