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

TL;DR

This paper develops a neural-network-based potential to study W-Cu alloys, revealing how composition affects their structural and mechanical properties across a wide temperature and pressure range.

Contribution

It introduces a novel deep potential model trained on DFT data for W-Cu compounds, enabling detailed simulation of their properties and deformation behaviors.

Findings

Bulk and Young's moduli decrease linearly with Cu content.

Higher Cu content increases critical strain and reduces critical stress.

A brittle-to-ductile transition occurs at around 37.5 at.% Cu.

Abstract

Tungsten-copper (W-Cu) compounds are widely utilized in various industrial fields due to their exceptional mechanical properties. In this study, we have developed a neural-network-based deep potential (DP) model that covers a wide range of temperatures, ranging from 0 to 3,000 K, and pressures, varying from 0 to 10 GPa. This study presents a model trained using density functional theory data for full concentration CuxW100-x compounds. Through this model, we systematically investigate the structural and mechanical properties of W-Cu alloys and have the following findings. First, 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 CuxW100-x compounds as the Cu concentration rises. Second, a higher Cu content results in higher critical strain and lower critical stress for these compounds. A brittle-to-ductile transition in the deformation mode predicted is predicted at around 37.5 at. % Cu content. Third, tensile loading tests in the W-Cu gradient structure reveal that Cu-poor region serves as a barrier, hindering shear band propagation while promoting new shear band formation in the Cu-rich region. The above results from the DP model are anticipated to aid in exploring the physical mechanisms underlying the complex phenomena of W-Cu systems and contribute to the advancement of methodologies for materials simulation.