Two-step machine learning enables optimized nanoparticle synthesis

• Main 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
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Materials; Computational Methods; Nanoparticles
• Online Access: https://hdl.handle.net/10356/151936
• Institution: Nanyang Technological University

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 de...