Two-step machine learning enables optimized nanoparticle synthesis
10.1038/s41524-021-00520-w
Saved in:
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: | COLLEGE OF DESIGN AND ENGINEERING |
Format: | Article |
Published: |
Nature Research
2022
|
Online Access: | https://scholarbank.nus.edu.sg/handle/10635/232749 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | National University of Singapore |
Similar Items
-
Two-step machine learning enables optimized nanoparticle synthesis
by: Mekki-Berrada, Flore, et al.
Published: (2021) -
Multi-fidelity high-throughput optimization of electrical conductivity in P3HT-CNT composites
by: Bash, Daniil, et al.
Published: (2022) -
Predicting synthesizability using machine learning on databases of existing inorganic materials
by: Zhu, Ruiming, et al.
Published: (2023) -
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains
by: Liang, Qiaohao, et al.
Published: (2022) -
Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
by: Hippalgaonkar, Kedar, et al.
Published: (2023)