Design of novel polymer-metal interfaces using first principles-informed artificial intelligence techniques

There is a growing interest in studying characteristics of polymer-metal interfaces, for the development of better dielectric polymers. Polymers have many advantages as dielectric materials due to their easy processing and flexibility but are limited by dielectric breakdown under high electric field that is driven via injection of hot carriers. In this work, we investigate how Aluminum and Boron-Nitride coating affects the charge injection barrier and hot carrier dynamics in various polymer systems. We develop a density functional theory-based method and design efficient ReaxFF force field using machine learning methods for simulating realistic interfaces. We illustrate this method by depositing bilayer Aluminum on various polymer slabs, such as Polycarbonate (PC), Polyethylene terephthalate (PET), Polypropylene (PP), and Polyethylene (PE) to investigate their dielectric performance. We further employ machine learning for studying interfacial structure dielectric breakdown field relationship to enhance design of efficient interfaces.