| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Materials Processing Fundamentals
|
| Presentation Title |
Machine Learning and Monte Carlo Simulations of the Gibbs Free Energy of the Fe-C System in a Magnetic Field |
| Author(s) |
Ming Li, Luke Wirth, Stephen Xie, Ajinkya Hire, Michele Campbell, Dallas Trinkle, Richard Hennig |
| On-Site Speaker (Planned) |
Ming Li |
| Abstract Scope |
Modeling the thermodynamics and kinetics of steels for designing processes in high magnetic fields requires knowledge of the magnetic Gibbs free energy, G. To obtain G, we first accelerate the energy evaluation of magnetic and atomic configurations by training an ultra-fast force field (UF3) machine learning potential on density-functional theory calculations with an applied magnetic field for various atomic and magnetic configurations of the bcc and fcc Fe-C phases. We show that the UF3 models trained and validated on this database accurately reproduce the potential energy landscapes as a function of the applied field. Thermodynamic integration using grand canonical Monte Carlo simulations utilizing the resulting UF3 energy model predicts the magnetic Gibbs free energy. We combine the simulations at different temperatures and fields to obtain a comprehensive model of the Gibbs free energy for the two phases as a function of temperature, atomic fraction of carbon, and magnetic field. |
| Proceedings Inclusion? |
Planned: |