2026-05-16T15:37:26Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:2101 2024-02-29T16:30:05Z openaire_data community-mcarchive

S. Ferrari, Brenda Manica, Matteo Giro, Ronaldo Laino, Teodoro B. Steiner, Mathias S. Ferrari, Brenda Manica, Matteo Giro, Ronaldo Laino, Teodoro B. Steiner, Mathias 2024-02-29 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. We curated a polymerization dataset for vinyl polymers covering reactions and retrosynthesis for representative homo-polymers and co-polymers. Overall, we report a forward model accuracy of 80% and a backward model accuracy of 60%. We further analyse the model performance on a set of case studies by providing polymerization and retro-synthesis examples and evaluating the model's predictions quality from a materials science perspective. text/csv text/csv application/zip text/csv text/markdown https://doi.org/10.24435/materialscloud:ef-4j oai:materialscloud.org:2101 mcid:2024.40 eng Materials Cloud https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:fn-r7 info:eu-repo/semantics/openAccess MIT License https://opensource.org/licenses/MIT License addendum polymerization reaction machine learning homopolymers co-polymers reactants reagents (solvents, catalysts) products Predicting polymerization reactions via transfer learning using chemical language models info:eu-repo/semantics/other