AI-Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers.

ABSTRACT: Vitrimer is a new, exciting class of sustainable polymers with healing abilities due to their dynamic covalent adaptive networks. However, a limited choice of constituent molecules restricts their property space and potential applications. To overcome this challenge, an innovative approach coupling molecular dynamics (MD) simulations and a novel graph variational autoencoder (VAE) model for inverse design of vitrimer chemistries with desired glass transition temperature (T_g) is presented. The first diverse vitrimer dataset of one million chemistries is curated and T_g for 8,424 of them is calculated by high-throughput MD simulations calibrated by a Gaussian process model. The proposed VAE employs dual graph encoders and a latent dimension overlapping scheme which allow

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC11809429 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature