| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
High Performance Steels
|
| Presentation Title |
A Neural Network Interatomic Potential for á-Fe-C-H Ternary System |
| Author(s) |
Fan-Shun Meng, Jun-Ping Du, Shuhei Shinzato, Nobuyuki Ishikawa, Kazuki Matsubara, Shigenobu Ogata |
| On-Site Speaker (Planned) |
Fan-Shun Meng |
| Abstract Scope |
A neural network interatomic potential (NNIP) for α-iron, carbon, and hydrogen ternary systems has been constructed aiming for understanding the thermodynamical behaviors of Fe-C steel in a hydrogen environment. The NNIP was trained using a database produced by spin-polarized density functional theory (DFT) calculations. The NNIP exhibits extraordinary performance in several scenarios which are frequently engaged in Fe and Fe-C in H environment studies, including the kinetics of H and C atoms in Fe and thermodynamic interaction of H and C atoms with iron vacancy, grain boundary, screw dislocation, cementite, and cementite-ferrite interfaces. Finally, molecular dynamics simulations for the clean and H or/and C decorated screw dislocation were performed to demonstrate the transferability and reliability of the potential. The effects of the C or/and H on the mobility of screw dislocation and the kink migration behavior of the clean and decorated dislocation were reported at the DFT level. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Iron and Steel, Machine Learning |