About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
Accelerated Discovery and Qualification of Nuclear Materials for Energy Applications
|
Presentation Title |
Point Defect Energies in Concentrated Alloys Using Ab Initio Calculations and Machine Learning |
Author(s) |
Anus Manzoor, Gaurav Arora, Dilpuneet S. Aidhy |
On-Site Speaker (Planned) |
Anus Manzoor |
Abstract Scope |
Concentrated alloys, including high entropy alloys, consist of multiple principal elements randomly distributed on a crystal lattice that causes large variations in point-defect formation and migration energies in a given alloy composition. Statistically capturing the variation requires performing large number of density functional theory calculations. The challenge is compounded due to the exponentially large number of compositions that are possible in these alloys. We solve the problem by leveraging machine learning tools where the defect energies computed from binary alloys are used to train the models to predict energies in multi-element alloys. We demonstrate accurate predictions of migration barriers in FeNiCr, and vacancy formation energies in NiCuAu. A major benefit of this approach is that once the binary database is built and the model is trained, defect energies can be easily predicted thereby bypassing the need to perform large number of calculations every time a new composition is discovered. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Nuclear Materials |