Neural ordinary differential equations for predicting the temporal dynamics of a ZnO solid electrolyte FET

Gaurav, A., Song, X., Manhas, S.K. et al. (1 more author) (2025) Neural ordinary differential equations for predicting the temporal dynamics of a ZnO solid electrolyte FET. Journal of Materials Chemistry C, 13 (6). pp. 2804-2813. ISSN 2050-7526

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Item Type: Article
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© The Royal Society of Chemistry 2024. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. http://creativecommons.org/licenses/by-nc/3.0/

Keywords: Macromolecular and Materials Chemistry; Engineering; Chemical Sciences; Networking and Information Technology R&D (NITRD); Affordable and Clean Energy
Dates:
  • Published: 14 February 2025
  • Published (online): 7 December 2024
  • Accepted: 6 December 2024
  • Submitted: 28 August 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering
Depositing User: Symplectic Sheffield
Date Deposited: 18 Dec 2024 10:01
Last Modified: 13 Mar 2025 09:48
Status: Published
Publisher: Royal Society of Chemistry (RSC)
Refereed: Yes
Identification Number: 10.1039/d4tc03696d
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 7: Affordable and Clean Energy
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