高导电性P3HT-CNT复合薄膜的机器学习和高通量稳健设计

Daniil Bash, Yongqiang Cai, Vijila Chellappan, S. L. Wong, Yang Xu, Pawan Kumar, J. Tan, Anas Abutaha, J. Cheng, Y. Lim, S. Tian, D. Ren, Flore Mekki-Barrada, W. Wong, J. Kumar, Saif A. Khan, Qianxiao Li, T. Buonassisi, K. Hippalgaonkar
{"title":"高导电性P3HT-CNT复合薄膜的机器学习和高通量稳健设计","authors":"Daniil Bash, Yongqiang Cai, Vijila Chellappan, S. L. Wong, Yang Xu, Pawan Kumar, J. Tan, Anas Abutaha, J. Cheng, Y. Lim, S. Tian, D. Ren, Flore Mekki-Barrada, W. Wong, J. Kumar, Saif A. Khan, Qianxiao Li, T. Buonassisi, K. Hippalgaonkar","doi":"10.26434/chemrxiv.13265288.v1","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":8423,"journal":{"name":"arXiv: Applied Physics","volume":"17 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Machine Learning and High-Throughput Robust Design of P3HT-CNT Composite Thin Films for High Electrical Conductivity\",\"authors\":\"Daniil Bash, Yongqiang Cai, Vijila Chellappan, S. L. Wong, Yang Xu, Pawan Kumar, J. Tan, Anas Abutaha, J. Cheng, Y. Lim, S. Tian, D. Ren, Flore Mekki-Barrada, W. Wong, J. Kumar, Saif A. Khan, Qianxiao Li, T. Buonassisi, K. Hippalgaonkar\",\"doi\":\"10.26434/chemrxiv.13265288.v1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":8423,\"journal\":{\"name\":\"arXiv: Applied Physics\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv: Applied Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26434/chemrxiv.13265288.v1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: Applied Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26434/chemrxiv.13265288.v1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

将高通量实验与机器学习相结合,可以快速优化参数空间,以实现目标属性。在这项研究中,我们证明了机器学习与多标签数据集相结合,可以额外用于科学理解和假设检验。我们引入了一个自动化的流动系统,用于薄膜制备,具有高通量滴铸,然后是光学和电学性质的快速表征,能够在一天内完成一个周期的学习,完全标记约160个样品。我们将区域规则聚3-己基噻吩与各种碳纳米管结合,以实现高达1200s /cm的电导率。有趣的是,当10%的双壁碳纳米管与长单壁碳纳米管一起添加时,出现了一个非直观的局部最优,其电导率高达700 S/cm,我们随后用高保真光学表征解释了这一点。采用数据集重采样策略和基于图的回归使我们能够考虑相关多输出的实验成本和不确定性估计,并支持证明将电荷离域与电导率联系起来的假设。因此,我们提出了一个强大的机器学习驱动的高通量实验方案,可用于优化和理解复合材料或混合有机-无机材料的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning and High-Throughput Robust Design of P3HT-CNT Composite Thin Films for High Electrical Conductivity
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信