About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
Accelerated Discovery and Qualification of Nuclear Materials for Energy Applications
|
Presentation Title |
Machine Learning and Atomistic Modeling of Defect Diffusion in Concentrated Ni-Fe Alloys |
Author(s) |
Wenjiang Huang, Xian-Ming Bai |
On-Site Speaker (Planned) |
Xian-Ming Bai |
Abstract Scope |
Single-phase concentrated solid solution alloys including high entropy alloys are promising structural materials for various high-temperature applications including nuclear energy. Defect diffusion and evolution in these non-traditional alloys play central roles in governing their macroscopic properties. Here we use atomistic modeling and artificial neural network based machine learning method to study how the atomic configurations influence the vacancy diffusion in Ni-Fe concentrated alloys in the full composition range. Molecular dynamics are conducted to calculate the vacancy diffusivities in these alloys at different temperatures, alloy compositions, and atomic configurations. Based on many alloy properties obtained from atomistic modeling such as vacancy formation energy distribution, migration barrier distribution, short-range-order parameter, and heat of mixing, a machine learning based model concerning statistical uncertainties is developed to predict the vacancy diffusivities for different atomic configurations. The effects of these alloy properties on the vacancy diffusion are also analyzed from the machine learning model. |
Proceedings Inclusion? |
Planned: |