Leveraging large language models for predictive chemistry

Abstract Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemical knowledge for each application and, therefore, require specialized expertise to develop. Here we show that GPT-3, a large language model trained on vast amounts of text extracted from the Internet, can easily be adapted to solve various tasks in chemistry and materials science by fine-tuning it to answer chemical questions in natural language with the correct answer. We compared this approach with dedicated machine learning models for many…

• NS
  National Science Foundation
• GF
  Grantham Foundation for the Protection of the Environment
• ÉP
  École Polytechnique Fédérale de Lausanne
• SN
  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

  Awards: 180544, 51NF40-, 182892, 51NF40-182892