Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods

Barzdajn, B. orcid.org/0000-0002-3081-4131 and P Race, C. orcid.org/0000-0002-9775-687X (2025) Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods. Modelling and Simulation in Materials Science and Engineering, 33 (2). 025011. ISSN 0965-0393

Abstract

Metadata

Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2025 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Dates:
  • Published: 27 January 2025
  • Published (online): 27 January 2025
  • Accepted: 17 December 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > School of Chemical, Materials and Biological Engineering
Depositing User: Symplectic Sheffield
Date Deposited: 04 Feb 2025 14:32
Last Modified: 04 Feb 2025 14:32
Published Version: https://doi.org/10.1088/1361-651x/ada050
Status: Published
Publisher: IOP Publishing
Refereed: Yes
Identification Number: 10.1088/1361-651x/ada050
Open Archives Initiative ID (OAI ID):

Export

Statistics