  The design of high-entropy alloys (HEA) with desired properties is
challenging due to their large compositional space. While various machine
learning (ML) models can predict specific HEA solid-solution phases (SS),
predicting high-entropy intermetallic phases (IM) is underdeveloped due to
limited datasets and inadequate ML features. This paper introduces feature
engineering-assisted ML models that achieve detailed phase classification and
high accuracy. By combining phase-diagram-based and physics-based features, it
is found that the ML models trained on the Random Forest (RF) and Support
Vector Machine (SVM) regressors, are able to classify individual SS and common
IM (Sigma, Laves, Heusler, and refractory B2 phases) with accuracies ranging
from 80 - 94%. The machine-learned features also enable the interpretation of
IM formation. Furthermore, the efficacies of the RF, SVM, and neural network
(NN) models are critically evaluated. The phase classification accuracies are
found to decrease upon utilizing the NN model to train the datasets. The
accuracy of the model prediction is validated by synthesizing 86 new alloys.
This approach provides a practical and robust framework for guiding HEA phase
design, particularly for technologically significant IM phases.
