About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Predicting the Energetics and Kinetics of Cr Atoms in Fe-Ni-Cr Alloys via Physics-based Machine Learning |
Author(s) |
Yuchu Wang, Yue Fan |
On-Site Speaker (Planned) |
Yuchu Wang |
Abstract Scope |
A fundamental understanding of Cr atoms' energetics and kinetics in Fe-Ni-Cr alloys is of crucial importance because it determines the mechanical and corrosion-resistant performance of Cr-Ni based austenitic stainless steels. Here we investigate the energy and activation barrier distributions of Cr atoms in austenitic alloys over a multiplicity of modeling samples across a wide range of chemical (e.g. solid solutions vs. segregated states) and microstructural (e.g. bulk vs. grain boundaries) environments. Assisted with energy landscape-sampling algorithm and physics-based machine learning, the thermodynamic and kinetic behaviors of Cr atoms are reliably predicted according to their local electronegativity and free volume of local atomic packing. In general, the stability is more sensitive to local electronegativity, while the mobility is more responsive to local free volume. The corresponding predictive maps in the parameter space are further established, and the insights into the design of austenitic alloys with desired properties are also discussed. |
Proceedings Inclusion? |
Planned: |
Keywords |
Modeling and Simulation, Machine Learning, Other |