About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Examining the Alpha-epsilon Transition in Iron Using Molecular-spin Dynamics |
Author(s) |
Svetoslav Nikolov, Andrew Rohskopf, Julien Tranchida, Kushal Ramakrishna, Attila Cangi, Mitchell Wood |
On-Site Speaker (Planned) |
Svetoslav Nikolov |
Abstract Scope |
For magnetic materials, like iron, that exhibit strong spin-orbit coupling, resolving the magnetic degrees of freedom is critical for understanding the corresponding thermal and mechanical responses. In the current effort, we utilize a machine-learned framework to train a SNAP interactomic potential and biquadratic spin exchange Hamiltonian on first principles high temperature/pressure data. The trained multi-potential model is then applied in a molecular-spin dynamics simulations, where we examine how the phonon density of states and thermal conductivities vary throughout the alpha-epsilon transition of iron. The spin dynamics model is modified to enable longitudinal spin fluctuations, whose impact on the alpha-epsilon transition is assessed. |
Proceedings Inclusion? |
Planned: |
Keywords |
Magnetic Materials, Modeling and Simulation, Machine Learning |