Physics‐Based Machine‐Learning Approach for Modeling the Temperature‐Dependent Yield Strength of Superalloys

In the pursuit of developing high‐temperature alloys with improved properties for meeting the performance requirements of next‐generation energy and aerospace demands, integrated computational materials engineering has played a crucial role. Herein, a machine learning approach is presented, capable of predicting the temperature‐dependent yield strengths of superalloys utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature, T_break, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black‐box approaches, our model is based on the underlying fundamental physics built directly into the model. A technique of global optimization, one allowing the concurrent optimization of model parameters over the low‐ and high‐temperature regimes, is presented. The results presented extend previous work on high‐entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature‐dependent strength behavior of superalloys as well as HEAs.