Segev Shlomov, Avi Yaeli
CHI 2024
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 an extension of transformer-based language models to polymerization for both reaction 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 examples and evaluate its prediction quality from a materials science perspective. To enable validation and reuse, we have made our models and data available in public repositories.
Segev Shlomov, Avi Yaeli
CHI 2024
Anurag Ajay, Seungwook Han, et al.
NeurIPS 2023
Daniel Karl I. Weidele, Hendrik Strobelt, et al.
SysML 2019
Ankit Vishnubhotla, Charlotte Loh, et al.
NeurIPS 2023