Overall energy density of nanoparticle-based functional composites can be enhanced by tailoring their constituent dielectric properties, breakdown strength, and interfacial polarisation. This project will develop predictive approaches for the enhancement of energy density in functional composites by combining a new data-driven computing paradigm based on Bayesian inference (BI) and computational localisation-homogenisation approach. Bayesian inference will be employed to predict interfacial polarisation behaviour of composites from available molecular simulation data. The atomistically-informed interface laws will be propagated onto continuum models, and used to derive the macroscopic response of composites with error bars via a computational localisation-homogenisation procedure.
The chosen PhD student will also be incorporated into our interdisciplinary Multiscale Materials Modelling Group at Warwick whose research is on related multiscale and multiphysics problems for advanced functional composite materials for energy-related applications.