Structure prediction in high-entropy alloys with machine learning

High-entropy alloy is an alloy design concept without a principal component. This concept not only refers to the complexity of alloy compositions but also means that when the high-entropy alloy transits from a high-energy state to low-energy state, there will be more intermediate metastable states. Corresponding to different states are the changes in the degree and manner of order in the microstructure. In this study, we used machine learning to combine elemental characteristics with long-term ordering and established 87% of prediction accuracy. This data-driven method can correlate elemental characteristics and metastable states and accelerate the discovery of potential compositions.

Automated summary

High-entropy alloy is a concept of alloy design that lacks principal components and has complex compositions and multiple intermediate metastable states. With machine learning, elemental characteristics can be combined with long-term ordering to successfully predict with 87% accuracy, accelerating the discovery of potential compositions.