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...
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) |
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Format: | Article |
Language: | English |
Published: |
Nature Research
2023
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Subjects: | |
Online Access: | View Fulltext in Publisher View in Scopus |
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