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
Abstract
© 2026 The Author(s). As the Version of Record of this article is going to be 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
Metadata
| Item Type: | Article |
|---|---|
| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © 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: |
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| 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 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:244341 |
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Licence: CC-BY 4.0

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