Accelerating material design with the generative toolkit for scientific discovery

Accelerating material design with the generative toolkit for scientific discovery

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 mat...

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Bibliographic Details
Main Authors: Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
Format: Article
Language:English
Published: Nature Portfolio 2023-05-01
Series:npj Computational Materials
Online Access:https://doi.org/10.1038/s41524-023-01028-1

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