Bruce Elmegreen, Hendrik Hamann, et al.
ICR 2023
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.
Bruce Elmegreen, Hendrik Hamann, et al.
ICR 2023
Jannis Born, Matteo Manica, et al.
iScience
Yunfei Teng, Anna Choromanska, et al.
ECML PKDD 2022
Jitendra Singh, Smit Marvaniya, et al.
INFORMS 2022