Identifying stress-induced heterogeneity in Cu₂₀Zr₂₀Ni₂₀Ti₂₀Pd₂₀ high-entropy metallic glass from machine learning atomic dynamics

Abstract

High-entropy metallic glasses (HEMGs) are amorphous alloys with a near-equiatomic composition containing at least five elements. Such a unique non-crystalline structure with high configurational entropy of mixing provides HEMGs with promising prospects in applications, and it also attracts great scientific interest. In this paper, we focused on the atomic mechanism of stress-induced heterogeneity in the Cu₂₀Zr₂₀Ni₂₀Ti₂₀Pd₂₀ HEMG. Applying the machine learning (ML) technique combined with the classical molecular dynamics (MD) simulation, we defined the liquid-like active atoms as the ones exhibiting high machine-learned temperature (T_ML). T_ML is a parameter to characterize the atomic motion activated by thermal and mechanical stimuli. The results reveal the stress-induced heterogeneity in atomic dynamics during creep. Local plastic flows originate from these active “hot” atoms, which have low five-fold symmetry, low coordination packing, and obvious chemical short-range ordering. Compared with conventional metallic glasses (MGs), the HEMG exhibits a smaller activation volume of creep, fewer active atoms, and sluggish dynamics. The results provide physical insights into the structural and dynamic heterogeneity in HEMGs at an atomic level.

Keywords

High-entropy metallic glasses / machine learning / k-nearest neighbor / molecular dynamics simulation / creep

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