Multi-fidelity high-throughput optimization of electrical conductivity in P3HT-CNT composites

Multi-fidelity high-throughput optimization of electrical conductivity in P3HT-CNT composites

Combining high-throughput experiments with machine learning accelerates materials and process optimization toward user-specified target properties. In this study, a rapid machine learning-driven automated flow mixing setup with a high-throughput drop-casting system is introduced for thin film prepar...

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Bibliographic Details
Main Authors: Bash, Daniil, Cai, Yongqiang, Chellappan, Vijila, Wong, Swee Liang, Xu, Yang, Kumar, Pawan, Tan, Jin Da, Abutaha, Anas, Cheng, Jayce J. W., Lim, Yee‐Fun, Tian, Siyu Isaac Parker, Ren, Zekun, Mekki‐Berrada, Flore, Wong, Wai Kuan, Xie, Jiaxun, Kumar, Jatin, Khan, Saif A., Li, Qianxiao, Buonassisi, Tonio, Hippalgaonkar, Kedar
Other Authors: School of Materials Science and Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Materials::Composite materials
Bayesian Optimization
Electrical Conductivity
Graphical Regression Models
High-Throughput Flow Mixing
Hypothesis Testing
Machine Learning
P3HT-CNT Composites
Online Access:https://hdl.handle.net/10356/156005

Internet

https://hdl.handle.net/10356/156005

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