Stacking Fault Energies in Ni-Based Concentrated Alloys Using Density Functional Theory and Machine Learning

Recent experimental work has shown that addition of certain elements can lower the stacking fault energy (SFE) of certain high entropy alloys thereby breaking the strength <i>vs</i> ductility tradeoff. To design alloys with desired SFEs, understanding the underlying mechanisms that control SFE is critical. In this work, using density functional theory (DFT), we isolate the effect of atomic radii, charge density, and nearest neighbor environment on the SFE for 3d, 4d, and 5d doped Ni and Cu alloys. Particularly, we find that planar charge density plays a much more significant role in defining the SFE in a particular matrix than any other variable, including atomic radius, in contrast to previous observations. The charge density then alloys to explain specific anomalies in SFE of Ni and Cu alloys such as to why a given alloying element results in opposite SFE trends in the two metals. Furthermore, we illustrate SFE predictions using machine learning modeling opening ways to predict SFE in concentrated alloys and narrow the uncertaintly observed widely in face centereed cubic concentrated alloys.