Journal Article

Two-step machine learning enables optimized nanoparticle synthesis

Zheng, Fang, Kedar, Hippalgaonkar, Tian Isaac Parker Siyu, Wang, Xiaonan, Mekki-Berrada Flore, Li Qianxiao, Huang, Tan, Senthilnath, Jayavelu, Mahfoud Zackaria, Buonassisi Tonio, Xie Jiaxun, Wong, Wai Kuan, Khan, Saif, Ren Zekun, Bash Daniil

2021

Overview

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.

• Publisher: Nature Publishing Group
• Subject: Absorbance / Artificial neural networks / Bayesian analysis / Chemical composition / Gaussian process / Learning algorithms / Machine learning / Materials science / Microfluidics / Nanomaterials / Nanoparticles / Nanotechnology / Neural networks / Optical properties / Optimization / Reaction synthesis / Silver