Refractory high-entropy alloys(RHEAs)typically exhibit a body-centered cubic(BCC)structure with excellent strength but poor ductility,which limits their practical applications.In this study,wedesigned BCC/FCC dual-pha...Refractory high-entropy alloys(RHEAs)typically exhibit a body-centered cubic(BCC)structure with excellent strength but poor ductility,which limits their practical applications.In this study,wedesigned BCC/FCC dual-phase RHEAs through phase diagram calculations and neural network modeling.The analysis of the binary phase formation relationships among alloying elements enabled the preliminary screening and inclusion of 13 liquid-phase-separated BCC/FCC dual-phase RHEAs in the training dataset for the machine learning model.Two strategic binary classifications of this dataset were conducted on HEAs to identify their“multiphase”and“solid solution”structures.Consequently,two neural network models were trained,achieving accuracies of 89.52%and 89.83%,respectively.These models predicted 51 BCC/FCC dual-phase RHEAs among 504 novel RHEAs,representing the first successful compositional design of metastable BCC/FCC dual-phase RHEAs.The arc-melted alloys exhibited refined dendritic structure.This study provides valuable insights for the tailored design of novel multi-phase RHEAs to achieve specific targeted properties.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2018YFB0703400)the National Natural Science Foundation of China(Grant No.51271034).
文摘Refractory high-entropy alloys(RHEAs)typically exhibit a body-centered cubic(BCC)structure with excellent strength but poor ductility,which limits their practical applications.In this study,wedesigned BCC/FCC dual-phase RHEAs through phase diagram calculations and neural network modeling.The analysis of the binary phase formation relationships among alloying elements enabled the preliminary screening and inclusion of 13 liquid-phase-separated BCC/FCC dual-phase RHEAs in the training dataset for the machine learning model.Two strategic binary classifications of this dataset were conducted on HEAs to identify their“multiphase”and“solid solution”structures.Consequently,two neural network models were trained,achieving accuracies of 89.52%and 89.83%,respectively.These models predicted 51 BCC/FCC dual-phase RHEAs among 504 novel RHEAs,representing the first successful compositional design of metastable BCC/FCC dual-phase RHEAs.The arc-melted alloys exhibited refined dendritic structure.This study provides valuable insights for the tailored design of novel multi-phase RHEAs to achieve specific targeted properties.
