Developing and Understanding Thermally Conductive Polymers by Combining Molecular Simulation, Machine Learning and Experiment

University Of Notre Dame, Notre Dame IN

Investigators

Abstract

Bulk amorphous polymers are usually thermal insulators with thermal conductivity (TC) in the range of 0.1-0.5 W/mK. However, they are widely used in heat transfer applications such as plastic heat exchangers, electronic cooling, and electric vehicle thermal management due to their mechanical flexibility and low cost. However, the development of thermally conductive amorphous polymers has been exceptionally challenging due to the lack of physical guidance. This project aims to combine molecular simulations, machine learning (ML), and experiments to develop amorphous polymers with high TC (>1.5 W/mK) and understand the underlying molecular features dictating thermal transport. The methodology developed and the physics understood from this project will significantly speed up and increase the success rate of developing high TC polymers. These new materials will contribute to tackling the challenges in many heat transfer applications. This project will also provide multi-disciplinary education opportunities to students and researchers from diverse backgrounds and scientific fields. The topics of this project will cultivate future workforce for the U.S. industry. The goal of this project is to significantly speed up the development of thermally conductive polymer and understand the underlying structure-property relationships governing amorphous polymer TC. To reach this goal, the objectives of this project are: (1) establish a standardized polymer database using high-throughput molecular dynamics (MD) simulations, complemented by data from existing database (e.g., PolyInfo) and open literature; (2) employ a unique ML training technique, semi-supervised framework for graph imbalanced regression (SGIR), to develop accurate surrogate models that map out structure-property relations for polymer chemistry, structure and TC, and use our Graph Rationalization with Environment-based Augmentations (GREA) technique to identify the influential molecular features impacting TC; (3) use the established model to predict ~100 polymers with varying TC and perform detailed MD simulations to calculate the TC to verify the prediction and understand the ML-identified structure-property relationship; (4) down-select 10 polymers with the expert opinion from collaborating polymer chemists on the synthesizability of the predicted polymers, synthesize them and measure their TC. The data-driven approach to be established in this project will provide an impactful example in the field concerning thermally conductive polymers. The established protocol can be followed to design materials with other desirable properties, impacting science and engineering fields beyond polymer or thermal transport. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.