Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites

Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites

Advanced Functional Materials

10.1002/adfm.202102606

http://dx.doi.org/10.1002/adfm.202102606

Daniil Bash, Yongqiang Cai, Vijila Chellappan, Swee Liang Wong, Xu Yang, Pawan Kumar, Jin Da Tan, Anas Abutaha, Jayce JW Cheng, Yee‐Fun Lim, Siyu Isaac Parker Tian, Zekun Ren, Flore Mekki‐Berrada, Wai Kuan Wong, Jiaxun Xie, Jatin Kumar, Saif A. Khan, Qianxiao Li, Tonio Buonassisi, Kedar Hippalgaonkar

Biomaterials, Electrochemistry, Electronic, Optical and Magnetic Materials, Condensed Matter Physics

24 June 2021

Bash, D., Cai, Y., Chellappan, V., Wong, S. L., Yang, X., Kumar, P., Tan, J. D., Abutaha, A., Cheng, J. J., Lim, Y., Tian, S. I. P., Ren, Z., Mekki‐Berrada, F., Wong, W. K., Xie, J., Kumar, J., Khan, S. A., Li, Q., Buonassisi, T., & Hippalgaonkar, K. (2021). Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites. Advanced Functional Materials, 31(36), 2102606. Portico. https://doi.org/10.1002/adfm.202102606

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 preparation, followed by fast characterization of proxy optical and target electrical properties that completes one cycle of learning with 160 unique samples in a single day, a >10× improvement relative to quantified, manual-controlled baseline. Regio-regular poly-3-hexylthiophene is combined with various types of carbon nanotubes, to identify the optimum composition and synthesis conditions to realize electrical conductivities as high as state-of-the-art 1000 S cm−1. The results are subsequently verified and explained using offline high-fidelity experiments. Graph-based model selection strategies with classical regression that optimize among multi-fidelity noisy input-output measurements are introduced. These strategies present a robust machine-learning driven high-throughput experimental scheme that can be effectively applied to understand, optimize, and design new materials and composites.

Publisher Copyright

This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Fund, Accelerated Materials Development for Manufacturing Program
Grant Reference no. : A1898b0043

This is the peer reviewed version of the following article: Bash, D., Cai, Y., Chellappan, V., Wong, S. L., Yang, X., Kumar, P., Tan, J. D., Abutaha, A., Cheng, J. J., Lim, Y., Tian, S. I. P., Ren, Z., Mekki‐Berrada, F., Wong, W. K., Xie, J., Kumar, J., Khan, S. A., Li, Q., Buonassisi, T., & Hippalgaonkar, K. (2021). Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites. Advanced Functional Materials, 31(36), 2102606. Portico. https://doi.org/10.1002/adfm.202102606, which has been published in final form at doi.org/10.1002/adfm.202102606. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

https://oar.a-star.edu.sg/communities-collections/articles/18522

1616-3028
1616-301X

Institute of Materials Research and Engineering