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Vitrimer is a new class of sustainable polymers with the ability ofself-healing through rearrangement of dynamic covalent adaptive networks.However, a limited choice of constituent molecules restricts their propertyspace, prohibiting full realization of their potential applications. Through acombination of molecular dynamics (MD) simulations and machine learning (ML),particularly a novel graph variational autoencoder (VAE) model, we establish amethod for generating novel vitrimers and guide their inverse design based ondesired glass transition temperature (Tg). We build the first vitrimer datasetof one million and calculate Tg on 8,424 of them by high-throughput MDsimulations calibrated by a Gaussian process model. The proposed VAE employsdual graph encoders and a latent dimension overlapping scheme which allows forindividual representation of multi-component vitrimers. By constructing acontinuous latent space containing necessary information of vitrimers, wedemonstrate high accuracy and efficiency of our framework in discovering novelvitrimers with desirable Tg beyond the training regime. The proposed vitrimerswith reasonable synthesizability cover a wide range of Tg and broaden thepotential widespread usage of vitrimeric materials.

Inverse Design of Vitrimeric Polymers by Molecular Dynamics and Generative Modeling