  Refractory high-entropy alloys (RHEAs) are a promising class of alloys that
show elevated-temperature yield strengths and have potential to use as
high-performance materials in gas turbine engines. However, exploring the vast
RHEA compositional space experimentally is challenging, and only a small
fraction of this space has been explored to date. The work demonstrates the
development of a state-of-the-art machine learning (ML) predictive 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 forward 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
development of a robust yield strength prediction model, the coupled framework
is used to discover new 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.
