Integrated design of aluminum-enriched high-entropy refractory B2 alloys with synergy of high strength and ductility.

Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility.

Predicting Yield Strength and Plastic Elongation in Body-Centered Cubic High-Entropy Alloys.

We employ machine learning (ML) to predict the yield stress and plastic strain of body-centered cubic (BCC) high-entropy alloys (HEAs) in the compression test. Our machine learning model leverages currently available databases of BCC and BCC+B2 entropy alloys, using feature engineering to capture electronic factors, atomic ordering from mixing enthalpy, and the D parameter related to stacking fault energy. The model achieves low Root Mean Square Errors (RMSE).