About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Vacancy Engineering in Metals and Alloys
|
| Presentation Title |
Modeling the Potential Energy Landscape of Vacancies in Nickel Superalloys Using Atomistic Simulations and Machine Learning |
| Author(s) |
Aditya Sundar, Michael Gao |
| On-Site Speaker (Planned) |
Aditya Sundar |
| Abstract Scope |
Vacancy diffusion majorly impacts the microstructure, mechanical properties, and oxidation behavior of alloys. Particularly, multi-element alloys are characterized by a wide distribution of vacancy formation energies and migration barriers, which can drastically modify their potential energy landscape and present fast diffusion pathways that can be activated at lower temperatures. Here, we present a detailed density functional theory based computational study to understand the impact of common transition metal and refractory alloying elements on the properties of vacancies in nickel superalloys. Building upon our previous work on vacancy formation energies, migration barriers in binary, ternary, and more complex alloys will be systematically studied to uncover the impact of each alloying element. The performance of various machine learning models and pre-trained universal force fields for the rapid prediction of these energy distributions will also be discussed. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Copper / Nickel / Cobalt, Modeling and Simulation, |