  High entropy alloys (HEA) show promise as a new type of high-performance
structural material. Their vast degrees of freedom provide for extensive
opportunities to design alloys with tailored properties. However, the
compositional complexities of HEAs present great challenges for alloy design.
Current approaches have shown limited reliability in accounting for the
compositional regions of single solid solution and composite phases. We present
a phenomenological method, analyzing binary phase diagrams to predict HEA phase
formation on the hypothesis that the structural stability of HEAs is encoded
within. Accordingly, we introduce a small number of phase-diagram inspired
parameters and employ machine learning to partition the formation region of
500+ reported HEA compositions. The model achieved a single phase HEA
prediction rate >80 %. To validate our method, we demonstrated the capability
of this method in predicting HEA solid solution phases with and without
intermetallics in 30 randomly selected complex compositions, with a success
rate of 77 %. The presented efficient search approach with high predictive
capability can be exploited to complement computation-intense methods in
providing a powerful platform for the design of high entropy alloys.
