Accelerating material design with the generative toolkit for scientific discovery

• Main Authors: Born, J. (Author), Buchan, M. (Author), Cadow, J. (Author), Chenthamarakshan, V. (Author), Christofidellis, D. (Author), Clarke, D. (Author), Das, P. (Author), Dave, A. (Author), Donovan, T. (Author), Giannone, G. (Author), Hamada, L. (Author), Hoffman, S.C (Author), Hsu, H.H (Author), Khrabrov, A. (Author), Kishimoto, A. (Author), Manica, M. (Author), McHugh, L. (Author), Padhi, I. (Author), Schilter, O. (Author), Smith, J.R (Author), Takeda, S. (Author), Teukam, Y.G.N (Author), Wehden, K. (Author), Zipoli, F. (Author)
• Format: Article
• Language: English
• Published: Nature Research 2023
• Subjects: Generative model; Materials design; Open-source libraries; Organic materials; Scientific discovery; Speed up; State of the art
• Online Access: View Fulltext in Publisher; View in Scopus

 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. © 2023, The Author(s).