Predicting polymerization reactions via transfer learning using chemical language models

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>S. Ferrari, Brenda</dc:creator>
  <dc:creator>Manica, Matteo</dc:creator>
  <dc:creator>Giro, Ronaldo</dc:creator>
  <dc:creator>Laino, Teodoro</dc:creator>
  <dc:creator>B. Steiner, Mathias</dc:creator>
  <dc:date>2023-09-06</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2023.137</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:zw-be</dc:identifier>
  <dc:identifier>mcid:2023.137</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1780</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>MIT License https://spdx.org/licenses/MIT.html</dc:rights>
  <dc:subject>polymerization reaction</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>homopolymers</dc:subject>
  <dc:subject>co-polymers</dc:subject>
  <dc:subject>reactants</dc:subject>
  <dc:subject>reagents (solvents, catalysts)</dc:subject>
  <dc:subject>products</dc:subject>
  <dc:title>Predicting polymerization reactions via transfer learning using chemical language models</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>