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

• 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
• ISSN: 2514-3255; 2514-3255
• DOI: 10.1049/nde2.12029

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.