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 material design.
@article{arxiv.2207.03928,
title = {Accelerating Material Design with the Generative Toolkit for Scientific Discovery},
author = {Matteo Manica and Jannis Born and Joris Cadow and Dimitrios Christofidellis and Ashish Dave and Dean Clarke and Yves Gaetan Nana Teukam and Giorgio Giannone and Samuel C. Hoffman and Matthew Buchan and Vijil Chenthamarakshan and Timothy Donovan and Hsiang Han Hsu and Federico Zipoli and Oliver Schilter and Akihiro Kishimoto and Lisa Hamada and Inkit Padhi and Karl Wehden and Lauren McHugh and Alexy Khrabrov and Payel Das and Seiji Takeda and John R. Smith},
journal= {arXiv preprint arXiv:2207.03928},
year = {2023}
}