Machine-learning-based intelligent framework for discovering refractory high-entropy alloys with improved high-temperature yield strength

Refractory high-entropy alloys (RHEAs) show significant elevated-temperature yield strengths and have potential to use as high-performance materials in gas turbine engines. Exploring the vast RHEA compositional space experimentally is challenging, and a small fraction of this space has been explored to date. This work demonstrates the development of a state-of-the-art machine learning framework coupled with optimization methods to intelligently explore the vast compositional space and drive the search in a direction that improves high-temperature yield strengths. Our yield strength model is shown to have a significantly improved predictive accuracy relative to the state-of-the-art approach, and also provides inherent uncertainty quantification through the use of repeated k-fold cross-validation. Upon developing and validating a robust yield strength prediction model, the coupled framework is used to discover RHEAs with superior high temperature yield strength. We have shown that RHEA compositions can be customized to have maximum yield strength at a specific temperature.

Automated summary

This study demonstrates a method of intelligently exploring the compositional space of refractory high-entropy alloys (RHEAs) through a machine learning framework and optimization methods, successfully discovering RHEA alloys with superior high-temperature yield strengths.