Oscar Sainz, Iker García-ferrero, et al.
ACL 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.
Oscar Sainz, Iker García-ferrero, et al.
ACL 2024
Raúl Fernández Díaz, Lam Thanh Hoang, et al.
ICLR 2025
Bruce Elmegreen, Hendrik Hamann, et al.
ICR 2023
Thomas Bohnstingl, Ayush Garg, et al.
ICASSP 2022