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Workshop: AI for Accelerated Materials Design (AI4Mat-2023)
Graph-to-String Variational Autoencoder for Synthetic Polymer Design
Gabriel Vogel · Paolo Sortino · Jana M. Weber
Keywords: [ transformers ] [ Variational Autoencoders ] [ synthetic polymers ] [ higher-order information ] [ generative molecular design ]
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.