Structural and mechanical properties of W-Cu compounds characterized by a neural-network-based potential

Abstract

We develop a neural-network deep potential (DP) model spanning 0–3,000 K and 0–10 GPa, trained on density functional theory data across the full concentration Cu_xW_100-x compounds. We systematically investigate the structural and mechanical properties of W-Cu alloys. The results show that the bulk modulus (B) and Young’s modulus (E) of W-Cu alloys exhibit a linear decline as the Cu content increases, indicating a softening trend in the Cu_xW_100-x compounds as the Cu concentration rises. Besides, a brittle-to-ductile transition in the deformation mode predicted is predicted at around 37.5 at. % Cu content. Moreover, tensile testing demonstrates that Cu-poor region effectively block shear band advancement, simultaneously stimulating nucleation of secondary shear bands in adjacent Cu-rich domains. The results are anticipated to aid in exploring the physical mechanisms underlying the complex phenomena of W-Cu systems.