@inproceedings{
vogel2023graphtostring,
title={Graph-to-String Variational Autoencoder for Synthetic Polymer Design},
author={Gabriel Vogel and Paolo Sortino and Jana Marie Weber},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=6E2qjEf7Rs}
}
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.
Graph-to-String Variational Autoencoder for Synthetic Polymer Design
Gabriel Vogel Paolo Sortino Jana Marie Weber