Physics-Based Machine-Learning Approach for Modeling the Temperature-Dependent Yield Strengths of Medium- or High-Entropy Alloys

Abstract

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