Machine learning of the architecture–property relationship in graft polymers

Abstract

Graft polymers are promising in energy and biomedical applications. However, the diverse architectures make it challenging to establish their structure–property relationships. We systematically investigate how backbone and side-chain architectures influence four key properties: glass transition temperature (T_g), self-diffusion coefficient (D), radius of gyration (R_g), and packing density (ρ). Using molecular dynamics simulations, we analyze a dataset of 500 graft polymers with randomly positioned side chains. T_g and D exhibit decoupled relationships due to the distinct topological effects. Furthermore, we develop dense neural networks (DNNs) and convolutional neural networks (CNNs) to pave the way to polymer design with desired properties.