Optimal design of γ'-strengthened high-entropy alloys via machine learning multilayer structural model

?'-strengthened high-entropy alloys (HEAs) have been widely studied in recent years because of their excellent mechanical properties at room- and elevated-temperature. The element diversity of HEAs leads to its vast composition and preparation process space and accelerating the design of ?'-strengthened HEAs by determining phase and mechanical properties remains a prominent challenge. In this study, by building a multi-layer structure prediction model, which includes accurate prediction models of microstructure and mechanical property, aiming to find HEAs with ?' phase high-volume fraction and high strength. Four ?'-strengthened alloys were selected from 800,000 candidate alloys by the multilayer structural prediction model, and then it was verified that all four HEAs have a high ?' phase volume fraction and high strength by experiment. Furthermore, the mathematical relationship between the different metal elements, heat treatment processes, and ?'phase volume fraction by resolving the machine learning model with the shapely additive algorithm (SHAP). A mathematical relationship model for the strengthening mechanism of HEAs was established to analyze the strengthening relationship of different strengthening mechanisms. The multilayer structural model can be used for the efficient design of ?'-strengthened high-entropy alloys, and analyze multiple potential relationships that influence the properties of alloys through the underlying data of the model.

Automated summary

In this study, a multi-layer structure prediction model was built to find HEAs with high ?' phase volume fraction and high strength. Four HEAs were selected from 800,000 candidate alloys by the model and experimentally verified to have high ?' phase volume fraction and high strength. Furthermore, a mathematical relationship model for the strengthening mechanism of HEAs was established using the machine learning model and the SHAP algorithm.