Comprehensive assessment of empirical potentials for molecular dynamics simulations of chromia
The importance of Cr 2 O 3 (chromia) lies in its ability to form protective layers on chromium-rich metallic alloys, which is utilised in the industry for constructing corrosion-resistant austenitic steels and nickel-based alloys. A better understanding of large defects in Cr 2 O 3 is critical because these defects play a crucial role in the growth kinetics of the protective chromia scale, influencing self-diffusion mechanisms, dominant defect types, and diffusion behaviour, all of which can influence the performance and durability of chromium-based alloys. This study presents a comprehensive evaluation of various empirical potentials for simulating the properties of Cr 2 O 3 in order to determine the best model to use to simulate extended defects. The assessment is focused on structural, thermodynamic, elastic, point defect, and grain boundary characteristics. An extensive literature review was conducted to compile a dataset for validating the available empirical potentials for Cr 2 O 3 from the literature. The evaluation of these empirical potentials provides valuable insights into their strengths and limitations, enabling researchers to make informed decisions when selecting appropriate potentials for simulating various properties of Cr 2 O 3 . This study's findings contribute to the ongoing efforts to improve the accuracy and reliability of computational materials science methods for predicting the behaviour of complex oxides. • Molecular dynamics simulationsusing empirical potentials are a valuable tool to investigate complex diffusion mechanisms. • Many empirical potentials are available for molecular dynamics simulations of chromia. • These potentials were assessed with a focus on their ability to reproduce crystal structures, defects and diffusion. • The most accurate potentials to simulate diffusion and complex defects structures in chromia were determined.
Authors
- Paul Fossati
Institutions
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris-Saclay (FR)
- CEA Paris-Saclay (FR)
Publication Details
- Journal
- Solid State Ionics
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1016/j.ssi.2024.116781
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52