Advisor: Prof. Rampi Ramprasad

will propose a doctoral thesis entitled,

ADVANCED POLYMER DESIGN FOR EXTREME ENVIRONMENTS:

                   HIGH-ENERGY DENSITY CAPACITORS AND AI-DRIVEN PROPERTY OPTIMIZATION

On

Friday, Feb. 14, 2024

9 am - 11 am

In 

MRDC Room 4211

or 

virtually via Teams

Committee:

Prof. Rampi Ramprasad- School of Materials Science and Engineering (advisor)

Prof. Naresh Thadani- School of Materials Science and Engineering

Prof. Chaitanya Deo- School of Materials Science and Engineering

Prof. Roshan Joseph- School of Industrial and Systems Engineering

Prof. Guoxiang (Emma) Hu- School of Materials Science and Engineering

Abstract: Designing functional polymers for specific applications is a highly complex and non-trivial challenge. The staggering diversity within the chemical space offers boundless potential, yet this very vastness transforms exploration into a daunting endeavor. The stakes are higher when engineering polymers for uncharted conditions, where no existing materials exhibit the necessary properties. In this work, we focus on one such extreme environment: polymer dielectric membranes for high-energy density capacitors operating at elevated temperatures. Most commercially available polymer dielectrics suffer from a significant decline in energy density as the temperature rises. To address these challenges, we introduce a cutting-edge polymer dielectric design pipeline, combining data-driven methods, machine learning, virtual reactions, and high-throughput simulations, culminating in experimental validation. Our approach rapidly screens millions of potential polymers, dramatically reducing the timeline from years to months. We trained ML models across several polymer classes, enabling fast and accurate property predictions. Additionally, our Molecular Dynamics (MD) simulation protocol predicts the glass transition temperature of polymers, providing an initial validation tool and a secondary data source to further train and refine our models. This integrated design approach not only accelerates design but also offers a scalable approach to tackle the design challenge for extreme environments.