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20201120-P3HT-CNT Manuscript SI combined chemrxiv.pdf (4.1 MB)

Machine Learning and High-Throughput Robust Design of P3HT-CNT Composite Thin Films for High Electrical Conductivity

preprint
submitted on 20.11.2020, 13:01 and posted on 23.11.2020, 09:42 by Daniil Bash, Yongqiang Cai, Chellappan Vijila, Swee Liang Wong, Yang Xu, Pawan Kumar, Jin Da Tan, Anas Abutaha, Jayce Jian Wei Cheng, Yee Fun Lim, Isaac Parker Siyu Tian, Zekun Ren, Wai Kuan Wong, Flore Mekki-Berrada, Jatin Nitin Kumar, Saif Khan, Qianxiao Li, Tonio Buonassisi, Kedar Hippalgaonkar

Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.

History

Email Address of Submitting Author

kedar@ntu.edu.sg

Institution

Nanyang Technological University

Country

Singapore

ORCID For Submitting Author

0000-0002-1270-9047

Declaration of Conflict of Interest

no conflict of interest

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