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Dissertation Proposal Defense – Deepak Kamal
MSE Grad Presentation
Tuesday, May 19, 2020 - 1:00pm
"Designing Polymers Resistant to Electric Field Extremes With Materials Modeling and Machine Learning"
Via Blue Jeans Video Conferencing https://bluejeans.com/819327060
Prof. Rampi Ramprasad, Advisor, MSE
Prof. David L. McDowell, ME/MSE
Prof. Juan-Pablo Correa-Baena, MSE
Prof. Seung Soon Jang, MSE
Prof. Roshan Joseph, ISyE
Polymers have found applications as dielectrics in high energy density capacitors owing to their low-cost, flexibility, attractive insulation properties, and ease of processability. However, their “energy density”, i.e., the maximum electrostatic energy that can be stored, is rather low in most commonly used polymer capacitor dielectrics; for instance, biaxially-oriented polypropylene (BOPP), the standard material used today in energy storage capacitors, displays an energy density of about 5 J/cc. The electrostatic energy density of a dielectric is directly controlled by its dielectric constant and the dielectric breakdown strength (Ebd), i.e., the maximum electric field the material can withstand. The goal of my work is to determine factors that affect Ebd in polymers and to use this understanding to discover new polymers with better electric field resistance. Specifically, the objectives of my work include the following:
- Identification of potential proxy properties (or “descriptors”) correlated to dielectric breakdown. Available experimental data on dielectric breakdown for a number of benchmark polymers will be utilized to reveal key descriptors. This step is essential because a search for polymers with high electric field resistance may be performed by devising screening criteria based on these proxy properties. Direct measurements or computation of the dielectric breakdown field for a large number of polymers is impractical at the present time, making the identification of the proxy properties critical.
- Development of reliable computational methods to determine these proxy properties for a set of candidate polymers. Density functional theory (DFT) based methodologies will lead to a fundamental understanding of the relationships between chemistry and the proxy properties on the one hand (e.g., “design guidelines”), and will also provide a dataset which can be used to build machine learning based prediction models of the proxy properties.
- Development of machine learning (ML) based prediction models of the proxy properties. These “surrogate models” will be trained on the DFT dataset and allow for the instantaneous predictions of the proxy properties for new cases. This will lead to a practical screening tool that can be aimed at a candidate set of polymers much larger than what DFT can handle.
- Creation of an AI-driven “autonomous” workflow for data creation and polymer design. “Active learning” based strategies will be adopted (and driven by the surrogate ML models) to steer the systematic and progressive creation of DFT data in regions of sparse knowledge and/or regions of attractive proxy properties.
Preliminary work has been performed pertaining to Objectives 1-3 above. The proxy properties identified thus far include the band gap and the electron injection barrier height at the polymer-metal electrode interface. Search for additional proxy properties is underway. The ultimate outcome of this effort will be: (1) a comprehensive and diverse DFT based polymer property dataset, (2) understanding of the chemical factors that govern the relevant proxy properties (and dielectric breakdown), (3) recommendations of polymers that may be resistant to extreme electric fields, and (4) a powerful ML-driven autonomous computational workflow extendable to study other problems.