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
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.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (54)
CITATIONS (3)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....