Deciphering Short-Range Order Driven Mechanical Enhancement in CrTaTiMo Refractory Alloys via Machine-learning Potential and Experimental Validation

This study employed a machine-learning potential to conduct large-scale molecular dynamics simulations on the CrTaTiMo refractory medium-entropy alloy (RMEA). The results show that the alloy's superior mechanical properties stem from its unique microstructure, where chemical segregation of Ta-Mo and Cr-Ti regions is driven by short-range ordering. The Ta-Mo-rich areas enhance elastic stiffness, while the Cr-Ti-rich regions promote deformation twinning and dislocation activity, leading to improved plasticity. This work provides crucial insights into the link between atomic-scale ordering and macroscale mechanical behavior in complex refractory alloys, guiding the design of advanced materials for extreme environments.