About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Chemistry and Physics of Interfaces
|
| Presentation Title |
Oxygen Adsorption on Various BCC Fe Surfaces: Comparison between Reactive Force Field and Universal Machine Learning Potential |
| Author(s) |
Zixiong Wei |
| On-Site Speaker (Planned) |
Zixiong Wei |
| Abstract Scope |
Oxidation is one of the most fundamental physicochemical processes in nature, proceeding through discrete, atomistic events such as molecular adsorption and atomic diffusion. Accurately modeling oxidation at the atomic scale requires reliable interatomic potentials. Reactive force fields (ReaxFFs) have been widely used to simulate chemical reactions, including oxidation. More recently, universal machine learning potentials (uMLPs) have emerged, offering near-quantum accuracy with broad applicability. However, it remains unclear whether well-trained uMLPs can consistently outperform reaction-specific ReaxFFs. To address this question, we investigate oxygen adsorption on various body-centered cubic (BCC) iron surfaces—a critical initial step in the oxidation process. This study compares selected ReaxFFs and state-of-the-art uMLPs against first-principles calculations to benchmark their performance. By identifying the strengths and limitations of each interatomic potential, this work provides guidance for selecting and developing interatomic potentials in future large-scale, high-fidelity simulations of oxidation and related surface reactions. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Iron and Steel, Surface Modification and Coatings, Computational Materials Science & Engineering |