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

Bash Daniil; Cai Yongqiang; Chellappan Vijila; Wong Swee Liang; Yang Xu; 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 Qianxao; Buonassisi Tonio; Hippalgaonkar Kedar

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

机译：P3HT-CNT复合材料中电导率的多保真高通量优化

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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 10x 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.

机译：将高吞吐量实验与机器学习相结合加速了材料和过程优化对用户指定的目标属性。在这项研究中，引入了一种快速的机器学习驱动的自动流量混合装置，用于薄膜制备，然后进行薄膜准备，然后快速表征代理光学和目标电气性质，完成一个学习的一个循环与160个唯一样品在一天中，A＆相对于量化，手动控制基线的10x改善。 Regio-常规聚-3-己基噻吩与各种类型的碳纳米管组合，以鉴定最佳的组合物和合成条件，以实现高达最先进的1000Scm（-1）的电导率。随后使用离线高保真实验进行验证和解释结果。介绍了基于图形的模型选择策略，具有优化多保真度噪声输入输出测量的经典回归。这些策略具有强大的机器学习驱动的高通量实验方案，可以有效地应用于了解，优化和设计新材料和复合材料。

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来源
《Advanced Functional Materials》 |2021年第36期|2102606.1-2102606.12|共12页
作者
Bash Daniil; Cai Yongqiang; Chellappan Vijila; Wong Swee Liang; Yang Xu; 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 Qianxao; Buonassisi Tonio; Hippalgaonkar Kedar;
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ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore|Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore;

Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore|Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore;

Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore|Singapore MIT Alliance Res & Technol Create Way 10-01 & 09-03 CREATE Tower Singapore 138602 Singapore;

Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore|Singapore MIT Alliance Res & Technol Create Way 10-01 & 09-03 CREATE Tower Singapore 138602 Singapore;

Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore;

Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore;

Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore;

Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore;

Natl Univ Singapore 3 Sci Dr 3 Singapore 117543 Singapore|ASTAR Inst High Performance Comp IHPC 1 Fusionopolis Way 16-16 Connexis Singapore 138632 Singapore;

Singapore MIT Alliance Res & Technol Create Way 10-01 & 09-03 CREATE Tower Singapore 138602 Singapore;

ASTAR Inst Mat Res & Engn IMRE 08-03 2 Fusionopolis Way Singapore 0803 Singapore|Nanyang Technol Univ 50 Nanyang Ave 01-30 Gen Off Block N4-1 Singapore 639798 Singapore;

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正文语种 eng
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Bayesian optimization; electrical conductivity; graphical regression models; high-throughput flow mixing; hypothesis testing; machine learning; p3ht-cnt composites;

机译：贝叶斯优化;电导率;图形回归模型;高通量流动混合;假设检测;机器学习;P3HT-CNT复合材料;

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8. Longitudinal Electrical Conductivity of Metal Matrix Composites at Cryogenic Temperature in the Presence of a Longitudinal Magnetic Field. Part 1. Solution of the Linearized Boltzmann Equation. Part 2. Application of the Solution to Metal Matrix Composites, [R] . Roig, F. S., Schoutens, J. E. 1987

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

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