| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Thermodynamics and Kinetics of Alloys III
|
| Presentation Title |
Contributions to Diffusion in Complex Materials Quantified with Machine Learning |
| Author(s) |
Dallas R. Trinkle, Soham Chattopadhyay |
| 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 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 individual contributions to diffusion--called "kinosons"--and compute their statistical distribution to model a complex multicomponent alloy. Calculating kinosons is orders of magnitude more efficient than computing whole trajectories, and it elucidates kinetic mechanisms for diffusion. The density of kinosons with temperature leads to new accurate analytic models for macroscale diffusivity, and show 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, Machine Learning |