| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
|
| Presentation Title |
Developing data-driven dislocation mobility laws for BCC metals |
| Author(s) |
Nicole K. Aragon, David Montes de Oca Zapiain, Eric Rothchild, Hojun Lim |
| On-Site Speaker (Planned) |
Nicole K. Aragon |
| Abstract Scope |
Body-centered cubic metals, such as tantalum, exhibit complex deformation behavior with a significant strength-dependence on temperature and applied stress. To accurately model these materials, it is critical to incorporate accurate dislocation mobility, a fundamental property that determines several characteristics of the plastic deformation. Experimental measurement of dislocation mobility is exceptionally challenging, thus dislocation mobility is often described by an empirical model parameterized by molecular dynamics (MD) simulations. These models typically assume fixed model form and parameters are determined by limited dataset. In this work, we utilize genetic programming to perform symbolic regression to establish a phenomenological dislocation mobility law that can easily be incorporated into mesoscale dislocation dynamics models. The proposed model is trained on a comprehensive dataset of MD simulations that characterizes dislocation velocities as a function of stress and temperature. Finally, to leverage the developed model, dislocation dynamics simulations using the proposed mobility law will be presented. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
ICME, Machine Learning, Computational Materials Science & Engineering |