Basel Shbita, Pengyuan Li, et al.
ESWC 2026
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.
Basel Shbita, Pengyuan Li, et al.
ESWC 2026
Guillaume Buthmann, Tomoya Sakai, et al.
ICASSP 2025
Jihun Yun, Aurelie Lozano, et al.
NeurIPS 2021
Jung koo Kang
NeurIPS 2025