Review of machine learning-driven design of polymer-based dielectrics

IF 3.8 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Nanodielectrics Pub Date : 2021-11-02 DOI:10.1049/nde2.12029
Ming-Xiao Zhu, Ting Deng, Lei Dong, Ji-Ming Chen, Zhi-Min Dang
{"title":"Review of machine learning-driven design of polymer-based dielectrics","authors":"Ming-Xiao Zhu,&nbsp;Ting Deng,&nbsp;Lei Dong,&nbsp;Ji-Ming Chen,&nbsp;Zhi-Min Dang","doi":"10.1049/nde2.12029","DOIUrl":null,"url":null,"abstract":"<p>Polymer-based dielectrics are extensively applied in various electrical and electronic devices such as capacitors, power transmission cables and microchips, in which a variety of distinct performances such as the dielectric and thermal properties are desired. To fulfil these properties, the emerging machine learning (ML) technique has been used to establish a surrogate model for the structure–property linkage analysis, which provides an effective tool for the rational design of the chemical and morphological structure of polymers/nanocomposites. In this article, the authors reviewed the recent progress in the ML algorithms and their applications in the rational design of polymer-based dielectrics. The main routes for collecting training data including online libraries, experiments and high-throughput computations are first summarized. The fingerprints charactering the microstructures of polymers/nanocomposites are presented, followed by the illustration of ML models to establish a mapping between the fingerprinted input and the target properties. Further, inverse design methods such as evolution searching strategies and generative models are described, which are exploited to accelerate the discovery of new polymer-based dielectrics. Moreover, structure–property linkage analysis techniques such as Pearson correlation calculation, decision-tree-based methods and interpretable neural networks are summarized to identify the key features affecting the target properties. The future development prospects of the ML-driven design method for polymer-based dielectrics are also presented in this review.</p>","PeriodicalId":36855,"journal":{"name":"IET Nanodielectrics","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/nde2.12029","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Nanodielectrics","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/nde2.12029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 15

Abstract

Polymer-based dielectrics are extensively applied in various electrical and electronic devices such as capacitors, power transmission cables and microchips, in which a variety of distinct performances such as the dielectric and thermal properties are desired. To fulfil these properties, the emerging machine learning (ML) technique has been used to establish a surrogate model for the structure–property linkage analysis, which provides an effective tool for the rational design of the chemical and morphological structure of polymers/nanocomposites. In this article, the authors reviewed the recent progress in the ML algorithms and their applications in the rational design of polymer-based dielectrics. The main routes for collecting training data including online libraries, experiments and high-throughput computations are first summarized. The fingerprints charactering the microstructures of polymers/nanocomposites are presented, followed by the illustration of ML models to establish a mapping between the fingerprinted input and the target properties. Further, inverse design methods such as evolution searching strategies and generative models are described, which are exploited to accelerate the discovery of new polymer-based dielectrics. Moreover, structure–property linkage analysis techniques such as Pearson correlation calculation, decision-tree-based methods and interpretable neural networks are summarized to identify the key features affecting the target properties. The future development prospects of the ML-driven design method for polymer-based dielectrics are also presented in this review.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
聚合物基电介质的机器学习驱动设计综述
聚合物基电介质广泛应用于各种电气和电子设备,如电容器、输电电缆和微芯片,其中需要各种不同的性能,如介电性能和热性能。为了实现这些特性,新兴的机器学习(ML)技术已被用于建立结构-性能连接分析的替代模型,这为合理设计聚合物/纳米复合材料的化学和形态结构提供了一个有效的工具。在这篇文章中,作者回顾了ML算法的最新进展及其在聚合物基电介质合理设计中的应用。首先总结了收集训练数据的主要途径,包括在线库、实验和高通量计算。给出了表征聚合物/纳米复合材料微观结构的指纹,然后举例说明了ML模型,以建立指纹输入和目标特性之间的映射。此外,还描述了进化搜索策略和生成模型等逆向设计方法,这些方法被用来加速新的聚合物基电介质的发现。此外,还总结了结构-性能链接分析技术,如Pearson相关计算、基于决策树的方法和可解释神经网络,以识别影响目标性能的关键特征。本文还介绍了聚合物基电介质ML驱动设计方法的未来发展前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IET Nanodielectrics
IET Nanodielectrics Materials Science-Materials Chemistry
CiteScore
5.60
自引率
3.70%
发文量
7
审稿时长
21 weeks
期刊最新文献
A combined technique for power transformer fault diagnosis based on k-means clustering and support vector machine Stability of giant dielectric properties in co‐doped rutile TiO2 ceramics under temperature and humidity High‐performance sulphur dioxide sensor: Unveiling the potential of photonic crystal fibre technology Improvement in non-linear electrical conductivity of silicone rubber by incorporating zinc oxide fillers and grafting small polar molecules Traditional fault diagnosis methods for mineral oil-immersed power transformer based on dissolved gas analysis: Past, present and future
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1