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
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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.

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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。
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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
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