Accelerated Discovery of Dielectric Polymer Materials Using Graph Convolutional Neural Networks

Polynorbornene (PNB) is an important amorphous polymer system, which has potential applications as a high energy density polymer due to its high breakdown strength with low dielectric loss and high thermal stability. Moreover, electrical properties of PNB can be significantly enhanced by incorporation of defects or synthesis with controlled crystallinity by hydrogenation reaction. However, this process is challenging since it involves experimental synthesis and characterization of combinatorial large number of polymer systems to identify potential candidates. Here, we propose a deep learning-based graph convolutional neural network (GNN) model that can identify polymer systems capable of exhibiting increased energy and power density. The GNN model is trained to predict dielectric constant for a polymer, where the training data for the high frequency dielectric constant of the PNB polymers are computed via ab-initio molecular dynamics simulation. Our model can significantly aid experimental synthesis of potentially new dielectric polymer materials which is otherwise difficult using simplistic statistical procedures.