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

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...

Full description

Saved in:
Bibliographic Details
Published in:IET Nanodielectrics 2022-03, Vol.5 (1), p.24-38
Main Authors: Zhu, Ming‐Xiao, Deng, Ting, Dong, Lei, Chen, Ji‐Ming, Dang, Zhi‐Min
Format: Article
Language:English
Subjects:
Algorithms
Datasets
Design analysis
Design techniques
Dielectric properties
Electric cables
Energy storage
Experiments
fingerprinting
Genomes
Heat conductivity
Inverse design
Machine learning
Methods
Nanocomposites
Neural networks
Polymers
polymer‐based dielectrics
structure‐property linkage analysis
Thermodynamic properties
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2514-3255
2514-3255
DOI:10.1049/nde2.12029