(Invited) Combining Machine Learning, DFT, EFM, and Modeling to Design Nanodielectric Behavior

Predicting and designing the properties of polymer nanodielectrics is challenging due to the number of parameters controlling properties and the breadth of scale (from electronic to mm). This paper summarizes a preliminary study using elongated semiconducting nanoparticles with an extrinsic interface that enhanced carrier trapping to attempt to find a parameter space that allows for improved permittivity and breakdown strength without increasing loss. We combine finite element modeling of dielectric constant with a Monte Carlo multi-scale simulation of carrier hopping to predict break down strength. Filler dispersion, filler geometry, isotropy and interface trapping properties are explicitly taken into account to compute design objectives associated with dielectric constant and mobility. Ultimately, we trained a latent variable Gaussian Process (LVGP) metamodel that can take both qualitative (e.g., orientation and dispersion states) and quantitative variables (e.g., microstructure descriptors) as inputs to predict properties over a broader range with observed tradeoffs.