Haohui Wang, Baoyu Jing, et al.
KDD 2024
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.
Haohui Wang, Baoyu Jing, et al.
KDD 2024
Alain Vaucher, Philippe Schwaller, et al.
AMLD EPFL 2022
Teng Xiao, Huaisheng Zhu, et al.
ICML 2024
Jiawei Zhou, Tahira Naseem, et al.
NAACL 2021