About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Vacancy Engineering in Metals and Alloys
|
| Presentation Title |
Vacancy-Mediated Diffusion in Complex Materials Quantified with Machine Learning |
| Author(s) |
Dallas R. Trinkle |
| On-Site Speaker (Planned) |
Dallas R. Trinkle |
| Abstract Scope |
The diffusivity of multicomponent or high-entropy alloys helps determine the range of stability of their solid solution phase. Computing the vacancy-mediated diffusivity of multicomponent alloys presents a challenge to sample the state space accurately and adequately to determine the transport coefficients. Using machine learning with a variational formula for diffusivity, we recast diffusion as a sum of kinosons and compute their statistical distribution to model a complex multicomponent alloy. Calculating kinosons is more efficient than computing whole trajectories, and elucidates how vacancies move in a complex energy landscape. The density of kinosons with temperature leads to new accurate analytic models for macroscale diffusivity, and shows sluggish diffusion for the fastest species. This novel approach provides both quantitatively accurate estimates of diffusion for significantly less effort, and a qualitative understanding of diffusivity in a complex system, making it useful for a wide variety of new problems in mass transport. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, |