Accelerating Material Design with the Generative Toolkit for Scientific Discovery
Matteo Manica
Jannis Born
Joris Cadow
Dimitrios Christofidellis
A. Dave
D. Clarke
Yves Gaëtan Nana Teukam
Giorgio Giannone
Samuel C. Hoffman
Matthew Buchan
Vijil Chenthamarakshan
Timothy Donovan
Hsiang-Han Hsu
F. Zipoli
Oliver Schilter
Akihiro Kishimoto
Lisa Hamada
Inkit Padhi
Karl Wehden
Lauren N. McHugh
Alexy Khrabrov
Payel Das
Seiji Takeda
John Smith

Abstract
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 material design.
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