Unique grain-boundary mediated plasticity: Deep Potential MD insights in HCP zinc

Abstract

Zinc (Zn) alloys are promising candidates for biodegradable medical applications. Their in-service performance depends critically on mechanical properties, particularly strain hardening/softening driven by grain boundary (GB) characteristics. Since the c/a ratio of hexagonal close-packed (HCP) Zn exceeds 1.8, classical interatomic potentials fail to accurately capture GB energetics and structures. In this work, a machine-learning Deep Potential (DP) model for Zn was developed. Using DP molecular dynamics (DeePMD), the energetics and structures of $ {<}11 \overline{2} 0{>} $ symmetrical tilt grain boundaries were analyzed across the full range of tilt angles (2θ = 0°~180°). Two distinct energy plateaus with minor oscillations were observed for 2θ in the ranges of 10°~90° and 96°~170°. Subsequently, DeePMD tensile simulations on the $ {<}11 \overline{2} 0{>} $ columnar grained (2D) Zn were performed to link GB energetics to plastic responses, revealing that grain rotation is the dominant plastic mechanism, facilitated by the observed energy plateaus and accompanied by limited basal dislocation activity. This grain-rotation-mediated plasticity was further confirmed by DeePMD in nanopolycrystalline (3D) Zn under tension, distinct from HCP Mg (c/a ≈ 1.63), which is dominated by multi-mode dislocations and twinning, and Be (c/a ≈ 1.52), where basal dislocations prevail. These findings highlight GB features as critical factors controlling plasticity and provide a theoretical foundation for designing nextgeneration biomedical alloys via crystallographic tailoring.

Keywords

Zinc / machine-learning potential / grain boundary / molecular dynamics / plastic mechanisms

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