Xu Han, Dongliang Zhang, et al.
Nature Communications
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.
Xu Han, Dongliang Zhang, et al.
Nature Communications
Skyler Speakman, Girmaw Abebe Tadesse, et al.
AMIA Annual Symposium 2021
Andrew Geng, Pin-Yu Chen
IEEE SaTML 2024
Kai Shen, Lingfei Wu, et al.
IJCAI 2020