  Vitrimer is a new class of sustainable polymers with the ability of
self-healing through rearrangement of dynamic covalent adaptive networks.
However, a limited choice of constituent molecules restricts their property
space, prohibiting full realization of their potential applications. Through a
combination of molecular dynamics (MD) simulations and machine learning (ML),
particularly a novel graph variational autoencoder (VAE) model, we establish a
method for generating novel vitrimers and guide their inverse design based on
desired glass transition temperature (Tg). We build the first vitrimer dataset
of one million and calculate Tg on 8,424 of them 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. By constructing a
continuous latent space containing necessary information of vitrimers, we
demonstrate high accuracy and efficiency of our framework in discovering novel
vitrimers with desirable Tg beyond the training regime. The proposed vitrimers
with reasonable synthesizability cover a wide range of Tg and broaden the
potential widespread usage of vitrimeric materials.
