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High entropy alloys (HEA) show promise as a new type of high-performancestructural material. Their vast degrees of freedom provide for extensiveopportunities to design alloys with tailored properties. However, thecompositional complexities of HEAs present great challenges for alloy design.Current approaches have shown limited reliability in accounting for thecompositional regions of single solid solution and composite phases. We presenta phenomenological method, analyzing binary phase diagrams to predict HEA phaseformation on the hypothesis that the structural stability of HEAs is encodedwithin. Accordingly, we introduce a small number of phase-diagram inspiredparameters and employ machine learning to partition the formation region of500+ reported HEA compositions. The model achieved a single phase HEAprediction rate >80 %. To validate our method, we demonstrated the capabilityof this method in predicting HEA solid solution phases with and withoutintermetallics in 30 randomly selected complex compositions, with a successrate of 77 %. The presented efficient search approach with high predictivecapability can be exploited to complement computation-intense methods inproviding a powerful platform for the design of high entropy alloys.

High Entropy Alloys Mined From Phase Diagrams