  Machine learning is becoming a powerful tool to 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, Tbreak, 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 form of a priori information. A technique
of 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 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 reveal 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 Tbreak. Once above Tbreak, phase transformations may start taking
place, and the alloy may begin losing structural integrity.
