Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface

Shahbazi, F., Esfahani, M.N., Keshmiri, A. et al. (1 more author) (2024) Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface. Scientific Reports, 14 (1). 20437. ISSN 2045-2322

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Item Type: Article
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© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Dates:
  • Published: 3 September 2024
  • Published (online): 3 September 2024
  • Accepted: 23 August 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 04 Oct 2024 09:31
Last Modified: 04 Oct 2024 09:31
Published Version: https://www.nature.com/articles/s41598-024-71007-z
Status: Published
Publisher: Springer Nature
Identification Number: 10.1038/s41598-024-71007-z
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