  In computational materials science, a common means for predicting macroscopic
(e.g., mechanical) properties of an alloy is to define a model using
combinations of descriptors that depend on some material properties (elastic
constants, misfit volumes, etc.), representative for the macroscopic behavior.
The material properties are usually computed using special quasi-random
structures (SQSs), in tandem with density functional theory (DFT). However, DFT
scales cubically with the number of atoms and is thus impractical for a
screening over many alloy compositions.
  Here, we present a novel methodology which combines modeling approaches and
machine-learning interatomic potentials. Machine-learning interatomic
potentials are orders of magnitude faster than DFT, while achieving similar
accuracy, allowing for a predictive and tractable high-throughput screening
over the whole alloy space. The proposed methodology is illustrated by
predicting the room temperature ductility of the medium-entropy alloy Mo-Nb-Ta.
