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Two-Step Machine Learning Enables Optimized Nanoparticle Synthesis

submitted on 20.07.2020, 03:11 and posted on 21.07.2020, 07:59 by 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

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 a 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.


Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043.


Email Address of Submitting Author


National University of Singapore



ORCID For Submitting Author


Declaration of Conflict of Interest

The authors declare no competing interests.

Version Notes

Version 1. The file includes a manuscript and supplementary information.