About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Local Chemical Ordering and Its Impact on Mechanical Behaviors, Radiation Damage, and Corrosion
|
| Presentation Title |
Chemical Short-Range Order and Kinetics of Diffusive Relaxation |
| Author(s) |
Ju Li |
| On-Site Speaker (Planned) |
Ju Li |
| Abstract Scope |
We present a deep reinforcement learning (DRL)-based computational framework, combined with a temporal difference (TD) learning method, to simulate long-timescale atomic processes of diffusive relaxation. We apply it to study the emergence of chemical short-range order (SRO) which plays a crucial role in unlocking unique material properties, and find that the proposed method effectively maps the relationship between time, temperature, and SRO change. By accelerating both the sampling of lower-energy states and the simulation of transition kinetics, we identify the thermodynamic limit and the role of kinetic trapping in the SRO. Furthermore, learning the mean first passage time to a given, target SRO relaxation allows capturing realistic timescales in diffusive atomistic rearrangements. This method offers valuable guidelines for optimizing material processing and extends atomistic simulations to previously inaccessible timescales, facilitating the study of slow, thermally activated processes essential for understanding and engineering material properties. [arXiv:2411.17839] |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Modeling and Simulation, |