Polymer Nanodielectrics are class of materials with intriguing combinations of properties. Predicting and designing the properties, however, is complex due to the number of parameters controlling the properties. This makes it difficult to compare results across groups, validate models, and develop a design methodology. This presentation will share a recent approach to developing a data driven design methodology grounded in physics-based models and experimental calibration. We combine finite element modeling of dielectric constant and loss functions with a Monte Carlo multi-scale simulation of carrier hopping to predict break down strength. Filler dispersion, filler geometry, isotropy and interface properties are explicitly taken into to account to compute objective functions for ideal nanodielectric insulators. Further, we have used machine learning to develop an EFM technique for directly measuring the dielectric constant of the nanoscale interfacial region as critical input to our models. Ultimately, we calculate the Pareto frontier with respect to nanocomposite constitute properties and geometry to optimize properties.

The finite element approach can be used to forward predict properties based on the properties of the interfacial region and filler dispersion, or as an inverse tool to calculate interface properties or develop optimized filler morphologies. The breakdown strength model critically depends on the energy distribution of trap states that inhibit space charge motion. We have developed an ab initio approach to determine the trap states at amorphous interfaces, and have used that to do a systematic analyses of trap distributions in composites with functionalized particle interfaces. The models all use 3D particle distributions based on 2D imaging (TEM) of the composites and publicly available tools for binarization and characterization of filler morphology. Finally, we use a design of experiment approach (Latin Hybercube Design) to sample the complete experimental design space, and using calibrated models and morphologies, create a data set spanning the full set of parameters. We use the data from the DOE to train a Gaussian Process metamodel and a genetic algorithm to determine which designs fulfill the design parameters. The talk will present several case studies as examples.

The data for this work was accessed from a new data resource, MaterialsMine, using FAIR (Findable, Accessible, Interoperable, and Reusable) principles. MaterialsMine is an open source data repository, and resource. It includes unique visualization tools as well as modeling and characterization resources.