npj Computational Materials (May 2023)

Accelerating material design with the generative toolkit for scientific discovery

  • Matteo Manica,
  • Jannis Born,
  • Joris Cadow,
  • Dimitrios Christofidellis,
  • Ashish Dave,
  • Dean Clarke,
  • Yves Gaetan Nana Teukam,
  • Giorgio Giannone,
  • Samuel C. Hoffman,
  • Matthew Buchan,
  • Vijil Chenthamarakshan,
  • Timothy Donovan,
  • Hsiang Han Hsu,
  • Federico Zipoli,
  • Oliver Schilter,
  • Akihiro Kishimoto,
  • Lisa Hamada,
  • Inkit Padhi,
  • Karl Wehden,
  • Lauren McHugh,
  • Alexy Khrabrov,
  • Payel Das,
  • Seiji Takeda,
  • John R. Smith

DOI
https://doi.org/10.1038/s41524-023-01028-1
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 6

Abstract

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Abstract With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.