| Issue |
MATEC Web Conf.
Volume 417, 2025
2025 RAPDASA-RobMech-PRASA-AMI Conference: Bridging the Gap between Industry & Academia - The 26th Annual International RAPDASA Conference, joined by RobMech, PRASA and AMI, co-hosted by CSIR and Tshwane University of Technology, Pretoria
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| Article Number | 02010 | |
| Number of page(s) | 8 | |
| Section | Computational & Data-driven Modelling seminar | |
| DOI | https://doi.org/10.1051/matecconf/202541702010 | |
| Published online | 25 November 2025 | |
Development of machine-learned force fields for lithium-manganese-oxide (Li-Mn-O) spinel. Density functional theory accuracy at a lower computational cost
Materials Modelling Centre, University of Limpopo, Private Bag x1106, Sovenga 0727, South Africa
* Corresponding author: donald.hlungwani@gmail.com
The on-the-fly machine learning implemented in the VASP code was utilized to generate interatomic potentials for LiMn2O4 spinel. The potentials will be further expanded to incorporate the Li2MnO3 layered character to enable accurate large-scale atomistic simulations of layered-spinel composite structures at practical computational cost. Li2MnO3 is incorporated in LiMn2O4 structures, which are plagued by capacity fading due to the loss of manganese caused by irreversible structural changes, to improve the electrochemical performance of this material. As such, the generated MLFFs in the current study are a significant step in that direction. The MLFFs were able to reproduce the DFT data with a percentage difference of less than 2%, showing impressive accuracy. The generated MLFFs will be further expanded and improved in a future study.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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