Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites
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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021
|
| Online Access: | https://hdl.handle.net/1721.1/138489 |
| _version_ | 1826199104481394688 |
|---|---|
| author | Bash, Daniil Cai, Yongqiang Chellappan, Vijila Wong, Swee Liang Yang, Xu Kumar, Pawan Tan, Jin Da Abutaha, Anas Cheng, Jayce JW 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 |
| author2 | Singapore-MIT Alliance in Research and Technology (SMART) |
| author_facet | Singapore-MIT Alliance in Research and Technology (SMART) Bash, Daniil Cai, Yongqiang Chellappan, Vijila Wong, Swee Liang Yang, Xu Kumar, Pawan Tan, Jin Da Abutaha, Anas Cheng, Jayce JW 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 |
| author_sort | Bash, Daniil |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:14:47Z |
| format | Article |
| id | mit-1721.1/138489 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:14:47Z |
| publishDate | 2021 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1384892023-04-14T19:28:51Z 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 JW 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 Singapore-MIT Alliance in Research and Technology (SMART) 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. 2021-12-15T17:11:46Z 2021-12-15T17:11:46Z 2021 2021-12-15T16:45:55Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/138489 Bash, Daniil, Cai, Yongqiang, Chellappan, Vijila, Wong, Swee Liang, Yang, Xu et al. 2021. "Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites." Advanced Functional Materials, 31 (36). en 10.1002/ADFM.202102606 Advanced Functional Materials Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley Other repository |
| spellingShingle | Bash, Daniil Cai, Yongqiang Chellappan, Vijila Wong, Swee Liang Yang, Xu Kumar, Pawan Tan, Jin Da Abutaha, Anas Cheng, Jayce JW 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 Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites |
| title | Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites |
| title_full | Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites |
| title_fullStr | Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites |
| title_full_unstemmed | Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites |
| title_short | Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites |
| title_sort | multi fidelity high throughput optimization of electrical conductivity in p3ht cnt composites |
| url | https://hdl.handle.net/1721.1/138489 |
| work_keys_str_mv | AT bashdaniil multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT caiyongqiang multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT chellappanvijila multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT wongsweeliang multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT yangxu multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT kumarpawan multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT tanjinda multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT abutahaanas multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT chengjaycejw multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT limyeefun multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT tiansiyuisaacparker multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT renzekun multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT mekkiberradaflore multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT wongwaikuan multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT xiejiaxun multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT kumarjatin multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT khansaifa multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT liqianxao multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT buonassisitonio multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites AT hippalgaonkarkedar multifidelityhighthroughputoptimizationofelectricalconductivityinp3htcntcomposites |