Sijia Liu, Pin-Yu Chen, et al.
IEEE SPM
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.
Sijia Liu, Pin-Yu Chen, et al.
IEEE SPM
Peihao Wang, Rameswar Panda, et al.
ICML 2023
Joao Lucas de Sousa Almeida, Arthur Cancelieri Pires, et al.
IEEE Transactions on Artificial Intelligence
Brian Quanz, Pavithra Harsha, et al.
INFORMS 2022