  High-entropy alloys, which exist in the high-dimensional composition space,
provide enormous unique opportunities for realizing unprecedented structural
and functional properties. A fundamental challenge, however, lies in how to
predict the specific alloy phases and desirable properties accurately. This
review article provides an overview of the data-driven methods published to
date to tackle this exponentially hard problem of designing high-entropy
alloys. Various utilizations of empirical parameters, first-principles and
thermodynamic calculations, statistical methods, and machine learning are
described. In an alternative method, the effectiveness of using
phenomenological features and data-inspired adaptive features in the prediction
of the high-entropy solid solution phases and intermetallic alloy composites is
demonstrated. The prospect of high-entropy alloys as a new class of functional
materials with improved properties is featured in light of entropic effects.
The successes, challenges, and limitations of the current high-entropy alloys
design are discussed, and some plausible future directions are presented.
