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 allows for individual representation of multi-component vitrimers. High accuracy and efficiency of the framework are demonstrated by discovering novel vitrimers with desirable T_g beyond the training regime. To validate the effectiveness of the framework in experiments, vitrimer chemistries are generated with a target T_g = 323 K. By incorporating chemical intuition, a novel vitrimer with T_g of 311–317 K is synthesized, experimentally demonstrating healability and flowability. The proposed framework offers an exciting tool for polymer chemists to design and synthesize novel, sustainable polymers for various applications.