|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Advances in Multi-Principal Elements Alloys X
||Machine learning enabled defect energies prediction in concentrated alloys
||Anus Manzoor, Gaurav Arora, Dilpuneet Aidhy
|On-Site Speaker (Planned)
Concentrated alloys, including high entropy alloys, consist of multiple principal elements randomly distributed on a crystal lattice that causes large variations in defect energies in a given alloy composition. Statistically capturing the variation requires performing large number of calculations which is computationally highly expensive. The challenge is compounded due to the exponentially large number of compositions that are possible in these alloys. Using machine learning model, we predict the defect energies in complex alloys using the database of the constituent’s binary alloys. Specifically, we demonstrate prediction of vacancy formation and vacancy migration energies in ternary, quaternary, and quinary in Ni-based alloys just by using the database of the binary alloys with high level of accuracy. A Major benefit of this approach is that for every new composition discovered, the defect energies can be computed using only the existing binary alloy database thereby completely bypassing the computationally expensive calculations.
||Computational Materials Science & Engineering, High-Entropy Alloys, Machine Learning