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. K E Y W O R D S fingerprinting, inverse design, machine learning, polymer-based dielectrics, structure-property linkage analysisThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Using a tailorable poly­(styrene-block-glycidyl methacrylate-block-methyl methacrylate) (PS-b-PGMA-b-PMMA) triblock terpolymer, the effect of changing the Flory–Huggins interaction parameters (χ) on the bulk self-assembly behavior of 12 linear ABC triblock terpolymers was investigated. By introducing different thiols to the short, reactive PGMA middle block, the experimental χAB and χBC could be arbitrarily altered, thereby changing the self-assembled morphologies that were characterized by small-angle X-ray scattering and transmission electron microscopy. Observed morphologies included lamellae, hexagonal cylinders, and tetragonal cylinders, which were found to be in close agreement with the expected morphologies obtained by self-consistent field theory simulation. Extending the 12 experimental results, 400 simulations were carried out to construct the χAB–χBC phase diagrams of the terpolymer at four different degrees of polymerization. The experimental and simulation results demonstrated that both the magnitude and the relation among each of the three interaction parameters are vital in determining the self-assembled morphology.