| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Understanding High Entropy Materials via Data Science and Computational Approaches
|
| Presentation Title |
Contributions to Diffusion in Complex Materials Quantified with Machine Learning |
| Author(s) |
Soham Chattopadhyay, Dallas Trinkle |
| On-Site Speaker (Planned) |
Soham Chattopadhyay |
| Abstract Scope |
Using machine learning with a variational formula for diffusivity, we recast diffusion as a sum of contributions from individual atomic migration events, called “kinosons”. This combination of machine learning with diffusion theory is exhibited by calculating atomic diffusivities in a complex high-entropy alloy. Calculating diffusivities using kinosons requires orders of magnitude lesser migration barrier calculations than computing whole trajectories using kinetic Monte Carlo simulations. Our approach also elucidates kinetic mechanisms for atomic diffusion in complex systems. Furthermore, studying the density of kinosons as a function of temperature leads to new accurate analytic models for macroscale diffusivity. |