Generative and multimodal AI for materials prediction and design: progress, challenges, and perspectives

Liu, X. orcid.org/0000-0002-3084-519X, Anjah, C., Jolly, B.E.E. orcid.org/0009-0006-1366-4094 et al. (9 more authors) (2026) Generative and multimodal AI for materials prediction and design: progress, challenges, and perspectives. Journal of Physics: Materials. ISSN: 2515-7639

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Item Type: Article
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© 2026 The Author(s). As the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 4.0 licence, this Accepted Manuscript is available for reuse under a CC BY 4.0 licence immediately. Everyone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by/4.0

Keywords: Multimodal Learning; Generative AI; Materials Design
Dates:
  • Submitted: 10 March 2026
  • Accepted: 3 August 2026
  • Published (online): 3 August 2026
  • Published: 3 August 2026
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > School of Chemical, Materials and Biological Engineering
The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematical and Physical Sciences
The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield)
Date Deposited: 12 Aug 2026 10:37
Last Modified: 12 Aug 2026 10:37
Status: Published online
Publisher: IOP Publishing
Refereed: Yes
Identification Number: 10.1088/2515-7639/ae93fd
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