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Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites
Author(s) -
Bash Daniil,
Cai Yongqiang,
Chellappan Vijila,
Wong Swee Liang,
Yang Xu,
Kumar Pawan,
Tan Jin Da,
Abutaha Anas,
Cheng Jayce JW,
Lim YeeFun,
Tian Siyu Isaac Parker,
Ren Zekun,
MekkiBerrada Flore,
Wong Wai Kuan,
Xie Jiaxun,
Kumar Jatin,
Khan Saif A.,
Li Qianxiao,
Buonassisi Tonio,
Hippalgaonkar Kedar
Publication year - 2021
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202102606
Subject(s) - materials science , throughput , carbon nanotube , high fidelity , fidelity , conductivity , computer science , composite material , nanotechnology , acoustics , telecommunications , chemistry , physics , wireless
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.