Elastic Constants in Ni Based High Entropy Alloys from DFT and Machine Learning

High entropy alloys (HEAs) present a paradigm shift in materials design. These alloys consist of multiple principal elements randomly distributed on a crystal lattice resulting in enormously large phase space which on the one hand presents large opportunities to unravel novel properties whereas on the other presents an equally large challenge to survey the phase space thereby presenting a data-science challenge. We present a machine learning framework coupled with electronic structure methods whereby properties in complex alloys could be predicted by learning from simpler alloys. The mechanical properties in complex alloys are predicted from the database of the constituent’s binary alloys. Specifically, we demonstrate predictions of stiffness constants, Young’s modulus, bulk and shear moduli, and Poisson’s ratio in ternary, quaternary, and quinary Ni-based alloys with a high-level of accuracy. A major benefit of this approach is that for every new composition discovered, the mechanical properties can be computed using only the existing binary alloy database thereby completely bypassing the computationally expensive calculations.