AI-guided inverse design and discovery of recyclable vitrimeric polymers

TL;DR

This paper introduces an AI-driven inverse design framework combining molecular simulations and a novel graph VAE to discover recyclable vitrimer polymers with targeted properties, validated by synthesis and experiments.

Contribution

It develops a large vitrimer dataset, a dual-encoder graph VAE, and demonstrates successful design and synthesis of new vitrimers with desired glass transition temperatures.

Findings

Created a dataset of 1 million vitrimer chemistries.

Achieved high accuracy in predicting Tg beyond training data.

Synthesized a vitrimer with Tg close to the target, demonstrating practical applicability.

Abstract

Vitrimer is a new, exciting class of sustainable polymers with the ability to heal due to their dynamic covalent adaptive network that can go through associative rearrangement reactions. However, a limited choice of constituent molecules restricts their property space, prohibiting full realization of their potential applications. To overcome this challenge, we couple molecular dynamics (MD) simulations and a novel graph variational autoencoder (VAE) machine learning model for inverse design of vitrimer chemistries with desired glass transition temperature (Tg) and synthesize a novel vitrimer polymer. We build the first vitrimer dataset of one million chemistries and calculate Tg on 8,424 of them by high-throughput MD simulations calibrated by a Gaussian process model. The proposed novel 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. To validate the effectiveness of our framework in experiments, we generate novel vitrimer chemistries with a target Tg = 323 K. By incorporating chemical intuition, we synthesize a vitrimer with Tg of 311-317 K, and experimentally demonstrate healability and flowability. The proposed framework offers an exciting tool for polymer chemists to design and synthesize novel, sustainable vitrimer polymers for a facet of applications.