  We combined descriptor-based analytical models for stiffness-matrix and
elastic-moduli with mean-field methods to accelerate assessment of
technologically useful properties of high-entropy alloys, such as strength and
ductility. Model training for elastic properties uses Sure-Independence
Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal
analytical model, constructed with meaningful atomic features to predict target
properties. Computationally inexpensive analytical descriptors were trained
using a database of the elastic properties determined from density functional
theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The
optimal Elastic-SISSO models, extracted from an exponentially large feature
space, give an extremely accurate prediction of target properties, similar to
or better than other models, with some verified from existing experiments. We
also show that electronegativity variance and elastic-moduli can directly
predict trends in ductility and yield strength of refractory HEAs, and reveals
promising alloy concentration regions.
