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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.

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Analysts frequently require data from multiple sources for their tasks, but finding these sources is challenging in exabyte-scale data lakes. In this paper, we address this problem for our enterprise's data lake by using machine-learning to identify related data sources. Leveraging queries made to the data lake over a month, we build a relevance model that determines whether two columns across two data streams are related or not. We then use the model to find relations at scale across tens of millions of column-pairs and thereafter construct a data relationship graph in a scalable fashion, processing a data lake that has 4.5 Petabytes of data in approximately 80 minutes. Using manually labeled datasets as ground-truth, we show that our techniques show improvements of at least 23% when compared to state-of-the-art methods.