High-entropy alloys (HEAs), or multi-principal element alloys (MPEAs), constitute a paradigmatic class of metallic materials whose entropy-stabilized solid solutions exhibit unique physical and chemical attributes unattainable in conventional alloys. Their exceptional capacity to reconcile strength and ductility under impulsive loading has positioned them at the forefront of impact engineering. Hitherto, investigations of dynamic mechanical response have focused almost exclusively on strain rate, at the expense of the enthalpy-entropy interplay that governs the coupled evolution of temperature, strain rate, and microstructure. Consequently, a rigorous correspondence between macroscopic stress-strain trajectories and the multiscale evolution of underlying microstructures remains elusive. Likewise, physics-based constitutive laws that incorporate temperature- and rate-sensitive kinetics remain in their infancy. Emerging evidence indicates that deliberate departure from equiatomic stoichiometry, together with microstructural architecting, can confer synergistic gains in mechanical properties within specific temperature-strain rate windows, surpassing the performance of conventional alloys. Yet the non-monotonic decline in flow stress with elevated temperature, particularly in the adiabatic regime prevailing at strain rates exceeding 10 3 s −1 , continues to elude quantitative interpretation, hindering a unified mechanistic understanding. This review thoroughly examines the extensive yet fragmented literature on the origins and effects of strain rate and temperature on the dynamic mechanical behavior of HEAs. We decipher dynamic plastic flow and fracture through the lens of microstructure, revealing how thermomechanical fluctuations govern the competitive activation of deformation mechanisms such as dislocation glide, twinning, phase transformation, and shear banding. Furthermore, we rigorously evaluate contemporary efforts to predict these behaviors using multiscale modeling, in situ diagnostics, and thermodynamics-informed machine learning. Finally, we advocate for a mechanism-guided, closed-loop design paradigm to extend the performance limits of these materials in extreme environments. By providing a critical assessment, this review aims to deliver a definitive roadmap to overcome theoretical barriers and experimental bottlenecks in the field.
Wang et al. (Wed,) studied this question.
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