This abstract addresses the application of machine learning (ML) to accelerate design of high-entropy alloys (HEAs), exhibiting exceptional material properties, especially at high temperatures. We address development of useful inverse design representations, and advanced physics-based metallurgical models, enabling identification of HEAs suitable for compressor blades of land-based turbines operating with ultra-high efficiency. By operating turbines with blades made of refractory HEAs at significantly-higher temperatures than conventional alloys, one can expect drastic improvements in efficiency. Assuming fossil-fuel combustion, the first stage of a modern turbine (the stage directly following the combustor) typically faces temperatures around 2,500°F (1,370°C). Modern military jet engines, like the Snecma M88, can experience turbine temperatures of 2,900°F (1,590°C). These high temperatures weaken the blades and make them more susceptible to creep failures. The high temperatures can also make the blades susceptible to corrosion failures. Finally, vibrations from the engine and the turbine itself can cause fatigue failures.