Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41524-021-00520-w
Title: Two-step machine learning enables optimized nanoparticle synthesis
Authors: Mekki-Berrada, Flore 
Ren, Zekun
Huang, Tan 
Wong, Wai Kuan 
Zheng, Fang 
Xie, Jiaxun 
Tian, Isaac Parker Siyu
Jayavelu, Senthilnath
Mahfoud, Zackaria
Bash, Daniil
Hippalgaonkar, Kedar
Khan, Saif 
Buonassisi, Tonio
Li, Qianxiao 
Wang, Xiaonan 
Issue Date: 20-Apr-2021
Publisher: Nature Research
Citation: Mekki-Berrada, Flore, Ren, Zekun, Huang, Tan, Wong, Wai Kuan, Zheng, Fang, Xie, Jiaxun, Tian, Isaac Parker Siyu, Jayavelu, Senthilnath, Mahfoud, Zackaria, Bash, Daniil, Hippalgaonkar, Kedar, Khan, Saif, Buonassisi, Tonio, Li, Qianxiao, Wang, Xiaonan (2021-04-20). Two-step machine learning enables optimized nanoparticle synthesis. npj Computational Materials 7 (1) : 55. ScholarBank@NUS Repository. https://doi.org/10.1038/s41524-021-00520-w
Rights: Attribution 4.0 International
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).
Source Title: npj Computational Materials
URI: https://scholarbank.nus.edu.sg/handle/10635/232749
ISSN: 2057-3960
DOI: 10.1038/s41524-021-00520-w
Rights: Attribution 4.0 International
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