|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Advances in Multi-Principal Elements Alloys X
||Factors affecting stacking fault energy in concentrated alloys using density functional theory and machine learning
||Gaurav Arora, Anus Manzoor, Dilpuneet Aidhy
|On-Site Speaker (Planned)
Recent experimental work has shown that the addition of certain elements can lower the stacking fault energy (SFE) of certain high entropy alloys thereby breaking the strength vs ductility tradeoff. To design alloys with desired SFEs, understanding the underlying mechanisms controlling SFE is critical. In this work, using density functional theory (DFT), we isolate the effect of atomic radii, planar charge density, and nearest neighbor environment on the SFE for 3d, 4d, and 5d doped Ni and Cu alloys. Particularly, we find that not only the radius of the dopant, but the planar charge density plays a significant role in defining the SFE in a particular matrix. Furthermore, we illustrate that using machine learning model, SFE for complex alloys can be predicted with high accuracy by using the database of the constituent’s binary alloys.
||Machine Learning, Modeling and Simulation, Copper / Nickel / Cobalt