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Machine learning accelerates discovery of high-temperature alloys

In a study recently published in Engineering, scientists from the University of Science and Technology Beijing, Guangdong Ocean University, and AiMaterials Research LLC have demonstrated a novel method to accelerate the discovery of refractory high-entropy alloys (RHEAs) compositions optimized for extreme conditions. The research, titled "Machine-Learning-Assisted Compositional Design of Refractory High-Entropy Alloys with Optimal Strength and Ductility," outlines how machine-learning (ML), genetic search, cluster analysis, and experimental design were employed to sift through billions of possible compositions and identify those with superior mechanical properties. The research team, led by Turab Lookman and Yanjing Su, synthesized and tested 24 different alloy compositions through a rigorous iterative process involving six feedback loops. Their efforts resulted in four compositions demonstrating remarkable high-temperature yield strength and room-temperature ductility. Among these, the ZrNbMoHfTa alloy system, specifically the composition Zr0.13Nb0.27Mo0.26Hf0.13Ta0.21, stood out with a yield strength approaching 940 MPa at 1200 °C and a room-temperature fracture strain of 17.2%.