Interfaces between different materials, for instance, electrode–dielectrics and vacuum/air/SF ₆ ‐insulators and oilpaper, are universal in high‐voltage insulation systems. Targeted tailoring of the interfacial properties, e.g. chemical structures, micro‐/nanoscale morphology, the charge injection barrier, trap states, and surface conductivity, is thought to be an effective approach to improve insulation properties such as breakdown and flashover strength, corona/tracking resistance, and wettability. In this article, the authors review recent progress in interface tailoring methods and their application to improve various insulation properties. The potential application scenarios of interface tailoring in different facilities are first summarised, and the desired insulation properties of various applications are highlighted. Interface tailoring methods are classified as physical/chemical surface modification and surface coating (inorganic, organic and composite), and the features of different techniques are described in detail. The structure–property relationships between interfacial states and various microscopic parameters such as charge injection barrier, trap distribution and surface conductivity are discussed together with the tailoring mechanisms for different insulation properties. Finally, the future development prospects of interface tailoring methods and their applications, e.g. multifunctional coating and stimuli–response smart coating, are presented.

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.

Epoxy–boron nitride composites are promising insulating materials, and it is highly important to understand their insulating performances at different temperatures with different nano-doping amounts. In this study, we investigated the effects of different mass fractions of epoxy–micron hexagonal boron nitride composites on their thermal conductivity, as well as the effects of temperature and mass fraction on their insulating performances. The results demonstrated that the thermal conductivity of epoxy–micron hexagonal boron nitride composites was superior to that of neat epoxy. The thermal conductivity of epoxy–micron hexagonal boron nitride composites increased with the mass fraction of hexagonal boron nitride, and their dielectric constant and dielectric loss increased with temperature. The dielectric constant of epoxy–micron hexagonal boron nitride composites decreased as the mass fraction of hexagonal boron nitride increased, while their dielectric losses decreased and then increased as the mass fraction of hexagonal boron nitride increased. Due to internal heat accumulation, the alternating current breakdown strength of epoxy–micron hexagonal boron nitride composites increased and then decreased as the mass fraction of hexagonal boron nitride increased. Additionally, as the temperature increased, the composites transitioned from the glassy state to the rubbery or viscous state, and the breakdown strength significantly degraded.

The interface between nanofillers and matrix plays a key role in determining the properties of nanocomposites, but the interfacial characteristics of nanocomposites such as molecular structure and interaction strength are not fully understood yet. In this work, the interfacial features of a typical nanocomposite, namely epoxy resin (EP) filled with boron nitride nanosheet (BNNS) are investigated by utilizing molecular dynamics simulation, and the effect of surface functionalization is analyzed. The radial distribution density (RDD) and interfacial binding energy (IBE) are used to explore the structure and bonding strength of nanocomposites interface. Besides, the interface compatibility and molecular chain mobility (MCM) of BNNS/EP nanocomposites are analyzed by cohesive energy density (CED), free volume fraction (FFV), and radial mean square displacement (RMSD). The results indicate that the interface region of BNNS/EP is composed of three regions including compact region, buffer region, and normal region. The structure at the interfacial region of nanocomposite is more compact, and the chain mobility is significantly lower than that of the EP away from the interface. Moreover, the interfacial interaction strength and compatibility increase with the functional density of BNNS functionalized by CH₃–(CH₂)₄–O– radicals. These results adequately illustrate interfacial characteristics of nanocomposites from atomic level.