About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
|
Chemistry and Physics of Interfaces
|
Presentation Title |
J-18: Stress Corrosion Cracking Simulation of High Entropy Alloys by Molecular Dynamics Method Based on Neural Network Potentials |
Author(s) |
Kai Nakajima, Ryutaro Kudo, Shogo Fukushima, Yixin Su, Yuta Asano, Yusuke Ootani, Nobuki Ozawa, Momoji Kubo |
On-Site Speaker (Planned) |
Kai Nakajima |
Abstract Scope |
High entropy alloys (HEAs) consist of five or more principal metallic elements. They have high mechanical properties and excellent corrosion resistance due to lattice distortion and, loose diffusion. However, in the presence of mechanical stress, fatal destruction of HEAs may occur due to the stress corrosion cracking (SCC) phenomenon. To further improve the reliability of HEAs and expand its applications, in-deep insights into the SCC mechanisms of HEAs are strongly desired, comprising atomic-scale deformation mechanisms and chemical reactions with corrosive environments.
In this study, we performed MD simulations of SCC in the typical FCC-type HEA, CoCrFeMnNi HEA. For handling multi-component systems accurately, we focused on neural network potentials. NNPs enable us to perform MD simulations with low computational costs while keeping the first-principles accuracy. The high precision MD method clarifies the mechanism of SCC of CoCrFeMnNi HEA and expands the potential of HEAs as a structural material. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, Machine Learning |