Accelerating material design with the generative toolkit for scientific discovery

Accelerating material design with the generative toolkit for scientific discovery

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 dis...

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Bibliographic Details
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
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