  Polymers are candidate materials for a wide range of sustainability
applications such as carbon capture and energy storage. However, computational
polymer discovery lacks automated analysis of reaction pathways and stability
assessment through retro-synthesis. Here, we report the first extension of
transformer-based language models to polymerization reactions for both forward
and retrosynthesis tasks. To that end, we have curated a polymerization dataset
for vinyl polymers covering reactions and retrosynthesis for representative
homo-polymers and co-polymers. Overall, we obtain a forward model Top-4
accuracy of 80% and a backward model Top-4 accuracy of 60%. We further analyze
the model performance with representative polymerization and retro-synthesis
examples and evaluate its prediction quality from a materials science
perspective.
