About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Accurate Fe–He Machine Learning Potential for Studying He Effects in Ferritic Steels for Fusion Applications |
Author(s) |
Krishna Chaitanya Pitike, Wahyu Setyawan |
On-Site Speaker (Planned) |
Krishna Chaitanya Pitike |
Abstract Scope |
Nanostructured Ferritic Alloys (NFAs) containing oxide nanoparticles are prospective advanced structural materials in future fusion reactors. Due to the lack of fusion test reactors, a predictive mesoscale model is required to understand radiation damage in NFAs, including helium bubble accumulation effects. We use machine learning interatomic potentials (MLPs), as they are highly accurate and computationally economical, compared to density functional theory (DFT). As a preliminary step, we develop a Fe–He MLP, based on ∼10,000 atomic configurations sampled using DFT. The developed MLP1 accurately predicts the bulk properties of BCC-Fe, He binding energies in HenV bubbles and Hen clusters. In the next step, we further include ~40,000 new atomic configurations to describe the thermodynamics of a generic HenVm bubble. The developed MLP2 is expected to describe the thermodynamics of any Helium bubble with accuracy comparable to DFT. The MLP2 can be further developed to include all chemical interactions in NFAs. |
Proceedings Inclusion? |
Planned: |
Keywords |
Nuclear Materials, Computational Materials Science & Engineering, Machine Learning |