Two-step machine learning enables optimized nanoparticle synthesis

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 wi...

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Autores principales:	Flore Mekki-Berrada, Zekun Ren, Tan Huang, Wai Kuan Wong, Fang Zheng, Jiaxun Xie, Isaac Parker Siyu Tian, Senthilnath Jayavelu, Zackaria Mahfoud, Daniil Bash, Kedar Hippalgaonkar, Saif Khan, Tonio Buonassisi, Qianxiao Li, Xiaonan Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/0ac9eaf9fc9e46a2ad63c0e8d956006b
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spelling	oai:doaj.org-article:0ac9eaf9fc9e46a2ad63c0e8d956006b2021-12-02T13:39:22ZTwo-step machine learning enables optimized nanoparticle synthesis10.1038/s41524-021-00520-w2057-3960https://doaj.org/article/0ac9eaf9fc9e46a2ad63c0e8d956006b2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00520-whttps://doaj.org/toc/2057-3960Abstract 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.Flore Mekki-BerradaZekun RenTan HuangWai Kuan WongFang ZhengJiaxun XieIsaac Parker Siyu TianSenthilnath JayaveluZackaria MahfoudDaniil BashKedar HippalgaonkarSaif KhanTonio BuonassisiQianxiao LiXiaonan WangNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Flore Mekki-Berrada Zekun Ren Tan Huang Wai Kuan Wong Fang Zheng Jiaxun Xie Isaac Parker Siyu Tian Senthilnath Jayavelu Zackaria Mahfoud Daniil Bash Kedar Hippalgaonkar Saif Khan Tonio Buonassisi Qianxiao Li Xiaonan Wang Two-step machine learning enables optimized nanoparticle synthesis
description	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.
format	article
author	Flore Mekki-Berrada Zekun Ren Tan Huang Wai Kuan Wong Fang Zheng Jiaxun Xie Isaac Parker Siyu Tian Senthilnath Jayavelu Zackaria Mahfoud Daniil Bash Kedar Hippalgaonkar Saif Khan Tonio Buonassisi Qianxiao Li Xiaonan Wang
author_facet	Flore Mekki-Berrada Zekun Ren Tan Huang Wai Kuan Wong Fang Zheng Jiaxun Xie Isaac Parker Siyu Tian Senthilnath Jayavelu Zackaria Mahfoud Daniil Bash Kedar Hippalgaonkar Saif Khan Tonio Buonassisi Qianxiao Li Xiaonan Wang
author_sort	Flore Mekki-Berrada
title	Two-step machine learning enables optimized nanoparticle synthesis
title_short	Two-step machine learning enables optimized nanoparticle synthesis
title_full	Two-step machine learning enables optimized nanoparticle synthesis
title_fullStr	Two-step machine learning enables optimized nanoparticle synthesis
title_full_unstemmed	Two-step machine learning enables optimized nanoparticle synthesis
title_sort	two-step machine learning enables optimized nanoparticle synthesis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0ac9eaf9fc9e46a2ad63c0e8d956006b
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Two-step machine learning enables optimized nanoparticle synthesis

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