Vinamra Baghel, Ayush Jain, et al.
INFORMS 2023
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.
Vinamra Baghel, Ayush Jain, et al.
INFORMS 2023
Trang H. Tran, Lam Nguyen, et al.
INFORMS 2022
Dimitrios Christofidellis, Giorgio Giannone, et al.
MRS Spring Meeting 2023
Fearghal O'Donncha, Malvern Madondo, et al.
AGU Fall 2022