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

• المؤلفون الرئيسيون: 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
• مؤلفون آخرون: School of Materials Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Materials::Composite materials; Bayesian Optimization; Electrical Conductivity; Graphical Regression Models; High-Throughput Flow Mixing; Hypothesis Testing; Machine Learning; P3HT-CNT Composites
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/156005

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...