A perspective on the data-driven design of polymer nanodielectrics

Abstract

Polymer nanodielectrics are an emerging class of materials with intriguing combinations of properties. They have application in everything from energy storage to high voltage electrical transmission, and energy generation. This article focuses on insulating nanodielectrics. In all cases, however, the complex set of parameters controlling the properties have made it difficult to both validate models and develop a design methodology. This paper demonstrates a recent approach to developing a data driven design methodology grounded in physics-based models and experimental calibration. Specifically, it combines finite element modeling of dielectric constant and loss functions with a Monte Carlo multi-scale simulation of carrier hopping to predict break down strength predictions. In both cases, the filler dispersion and interface properties are explicitly taken into to account to compute objective functions for ideal nanodielectric insulators. Using a Gaussian process for meta-modeling and multi-objective optimization of these computational predictions for polystyrene-silica composites, this paper identifies the Pareto frontiers with respect to loading and dispersion of nanofillers for maximizing breakdown strength and minimizing the dielectric constant and loss tangents.