Predicting polymerization reactions via transfer learning using chemical language models
Chemical Physics (physics.chem-ph)
QA76.75-76.765
Condensed Matter - Materials Science
Physics - Chemical Physics
TA401-492
Materials Science (cond-mat.mtrl-sci)
FOS: Physical sciences
Computer software
02 engineering and technology
0210 nano-technology
Materials of engineering and construction. Mechanics of materials
DOI:
10.1038/s41524-024-01304-8
Publication Date:
2024-06-04T15:28:02Z
AUTHORS (5)
ABSTRACT
AbstractPolymers 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.
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