Jiaqi Han, Wenbing Huang, et al.
NeurIPS 2022
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.
Jiaqi Han, Wenbing Huang, et al.
NeurIPS 2022
Pawan Chowdhary, Taiga Nakamura, et al.
INFORMS 2020
Dzung Phan, Vinicius Lima
INFORMS 2023
Parikshit Ram, Kenneth Clarkson, et al.
NeurIPS 2025