Graph-to-String Variational Autoencoder for Synthetic Polymer Design
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.
Cite
Text
Vogel et al. "Graph-to-String Variational Autoencoder for Synthetic Polymer Design." NeurIPS 2023 Workshops: AI4Mat, 2023.Markdown
[Vogel et al. "Graph-to-String Variational Autoencoder for Synthetic Polymer Design." NeurIPS 2023 Workshops: AI4Mat, 2023.](https://mlanthology.org/neuripsw/2023/vogel2023neuripsw-graphtostring/)BibTeX
@inproceedings{vogel2023neuripsw-graphtostring,
title = {{Graph-to-String Variational Autoencoder for Synthetic Polymer Design}},
author = {Vogel, Gabriel and Sortino, Paolo and Weber, Jana Marie},
booktitle = {NeurIPS 2023 Workshops: AI4Mat},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/vogel2023neuripsw-graphtostring/}
}