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arXiv · 2310.11423

Predicting polymerization reactions via transfer learning using chemical language models

Brenda S. Ferrari·Matteo Manica·Ronaldo Giro·Teodoro Laino·Mathias B. Steiner
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Abstract

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.

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Brenda S. Ferrari, Matteo Manica, Ronaldo Giro, Teodoro Laino, Mathias B. Steiner. 2023-10-17. Predicting polymerization reactions via transfer learning using chemical language models. https://arxiv.org/abs/2310.11423

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