Graph-to-String Variational Autoencoder for Synthetic Polymer Design
Submission Track: Papers
Submission Category: AI-Guided Design
Keywords: generative molecular design, synthetic polymers, higher-order information, variational autoencoders, transformers
TL;DR: Our graph-2-string variational autoencoder generates synthetic polymers including their higher-order structure.
Abstract: Generative molecular design is becoming an increasingly valuable approach to accelerate materials discovery. Besides comparably small amounts of polymer data, also the complex higher-order structure of synthetic polymers makes generative polymer design highly challenging. We build upon a recent polymer representation that includes stoichiometries and chain architectures of monomer ensembles and develop a novel variational autoencoder (VAE) architecture encoding a graph and decoding a string. Most notably, our model learns a latent space (LS) that enables de-novo generation of copolymer structures including different monomer stoichiometries and chain architectures.
Digital Discovery Special Issue: Yes
Submission Number: 9
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