Comprehensive evaluation and parametric sensitivity of interatomic potential models for diffusion kinetics of Cr 2 O 3 in molecular dynamics

Jiaqi Wang, Dongwon Shin, Seungha Shin

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

While molecular dynamics (MD) has proven to be a promising approach to investigate the diffusion properties, the grand challenge resides in evaluating potential model parameters to accurately replicate experimentally measured properties. The Buckingham potential model with Columbic interaction is widely employed in MD simulations of chromia (Cr 2 O 3 ) systems, as it allows for reasonable computational cost and accuracy. However, considering the well-known limitation of classical potential models in simultaneous reproduction of various physical phenomena, further comprehensive evaluation of the potential is required for calculation of diffusion properties. In this study, we benchmark the performance of three different Buckingham models with the experimental data by calculating structural, thermodynamic, and mechanical properties of defect-free Cr 2 O 3 , and diffusion properties of Cr 2 O 3 with vacancy defects. Available Buckingham models display limited accuracies, consolidating the necessity of retraining the potential parameters for all properties impacting the diffusion dynamics. Oversimplification in parameterization procedures is suggested to impede the universal performance in property reproduction. This research also demonstrates effective guidelines for choosing a proper parameter set of current Buckingham potential for MD simulation with Cr 2 O 3 depending on properties and for potential reparameterization.

Original languageEnglish
Article number015123
JournalAIP Advances
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2019

Funding

This work was supported by the US Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Propulsion Materials Program. This research used resources of the Oak Ridge Leadership Computing Facilities at Oak Ridge National Laboratory, which is supported by the Office of Science of the US DOE under contract DE-AC05-00OR22725. This work also utilized the resources of Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant number ACI-1053575.

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