TY - JOUR
T1 - Design of BCC/FCC dual-solid solution refractory high-entropy alloys through CALPHAD, machine learning and experimental methods
AU - He, Longjun
AU - Wang, Chaoyue
AU - Zhang, Mina
AU - Li, Jinghao
AU - Chen, Tianlun
AU - Zhou, Xianglin
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/4/20
Y1 - 2025/4/20
N2 - 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, we designed 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.
AB - 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, we designed 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.
UR - http://www.scopus.com/inward/record.url?scp=105003138810&partnerID=8YFLogxK
U2 - 10.1038/s41524-025-01597-3
DO - 10.1038/s41524-025-01597-3
M3 - Journal article
AN - SCOPUS:105003138810
SN - 2057-3960
VL - 11
JO - npj Computational Materials
JF - npj Computational Materials
IS - 1
M1 - 105
ER -