About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Chemistry and Physics of Interfaces
|
| Presentation Title |
Machine-Learned Atomistic Simulations Reveal Hydrogen-Resistant Grain Boundaries in Steel |
| Author(s) |
Shigenobu Ogata |
| On-Site Speaker (Planned) |
Shigenobu Ogata |
| Abstract Scope |
Hydrogen embrittlement often promotes intergranular fracture in high-strength steels, yet some grain-boundary segments remain resistant even under hydrogen charging. We present a computation-centered atomistic study of this contrast using a high-fidelity machine-learned Fe–H potential combined with hybrid grand-canonical Monte Carlo/molecular dynamics simulations. Experimentally informed bicrystal models are used to examine how hydrogen changes crack-tip deformation and fracture pathways. The simulations show that hydrogen resistance is not controlled by misorientation alone, but by competition among grain-boundary cleavage, dislocation emission and twinning-assisted fracture. A mechanism map based on this mode selection identifies boundary characters that can retain crack-tip plasticity under hydrogen. The results suggest a computational route toward grain-boundary engineering of hydrogen-resistant steels. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Iron and Steel, Mechanical Properties, Modeling and Simulation |