Machine learning-driven interfacial characterization and dielectric breakdown prediction in polymer nanocomposites

The development of polymer nanocomposites has emerged as a promising approach for achieving higher-density energy storage. However, challenges in directly characterizing the interface between the matrix and nanoparticles, a pivotal factor for performance enhancement, have led to a shortfall in effective modeling methods. In this work, we propose a novel interfacial modeling approach that quantitatively describes the continuous transition of dielectric properties across the interface, capturing the inhomogeneous nature observed experimentally. A finely tuned Polynomial Chaos Neural Network (PCNN) with a determination coefficient exceeding 0.999 is developed to elucidate the relationship between model parameters and nanocomposite permittivity. The finite element model employing the proposed interface model demonstrates improved accuracy in predicting the permittivity of various nanocomposite systems with a physical insight into the interface. Built upon the interface model, a developed phase field model is then incorporated to investigate the dielectric breakdown mechanism in nanocomposites, highlighting the interface's capacity to repel the breakdown path. 3D phase field simulations on electrical treeing successfully forecast the electrical tree structures in pure epoxy and nanocomposites with new insights into the dielectric breakdown. This research addresses a crucial need in the numerical modeling of nanocomposite interfaces and their role in dielectric breakdown analysis, providing a valuable tool for the design of next-generation dielectric materials with improved energy storage capabilities.