A physics-based machine-learning approach for modeling the temperature-dependent yield strengths of medium- or high-entropy alloys

Machine learning is becoming a powerful tool to accurately predict temperature-dependent yield strengths (YS) of structural materials, particularly for multi-principal-element systems. However, successful machine-learning predictions depend on the use of reasonable machine-learning models. Here, we present a comprehensive and up-to-date overview of a bilinear log model for predicting temperature-dependent YS of medium-entropy or high-entropy alloys (MEAs or HEAs). In this model, a break temperature, T_break, is introduced, which can guide the design of MEAs or HEAs with attractive high-temperature properties. Unlike assuming black-box structures, our model is based on the underlying physics, incorporated in the form of a priori information. A technique for the unconstrained global optimization is employed to enable the concurrent optimization of model parameters over low- and high-temperature regimes, showing that the break temperature is consistent across the YS and ultimate strength for a variety of HEA compositions. A high-level comparison between YS of MEAs/HEAs and those of Nickel-based superalloys reveals superior strength properties of selected refractory HEAs. For reliable operations, the temperature of a structural component, such as a turbine blade, made from refractory alloys, may need to stay below T_break. Once above T_break, phase transformations may start taking place, and the alloy may begin losing structural integrity.