Atomic cluster expansion interatomic potential for defects and thermodynamics of Cu-W system

TL;DR

This paper develops a machine learning interatomic potential for Cu-W alloys using atomic cluster expansion, accurately modeling properties and phase behaviors to understand defect formation and thermodynamics in this immiscible system.

Contribution

It introduces a novel ACE-based machine learning interatomic potential specifically for Cu-W, capturing fundamental properties and phase behaviors with high fidelity.

Findings

Accurately reproduces DFT-predicted properties of Cu and W.

Predicts phase separation and uphill diffusion phenomena.

Provides insights into atomistic mechanisms in Cu-W nano-multilayers.

Abstract

The unique properties exhibited in immiscible metals, such as excellent strength, hardness, and radiation-damage tolerance, have stimulated the interest of many researchers. As a typical immiscible metal system, the Cu-W nano-multilayers combine the plasticity of copper and the strength of tungsten, making it a suitable candidate for applications in aerospace, nuclear fusion engineering, and electronic packaging etc. To understand the atomistic origin of the defects and thermodynamics of the Cu-W immiscible system, we have developed an accurate machine learning interatomic potential (ML-IAP) for Cu-W based on the atomic cluster expansion (ACE) method. The Cu-W ACE potential can faithfully reproduce the fundamental properties of Cu and W predicted by density functional theory (DFT). Moreover, the thermodynamical properties, such as the melting point, coefficient of thermal expansion, diffusion coefficient, and equation of the state curve of the Cu-W solid solution, are calculated and compared against DFT and experiments. Monte Carlo Molecular Dynamics (MC-MD) simulations performed with the Cu-W ACE potential predict the experimentally observed phase separation and uphill diffusion phenomena. Our findings not only provide an accurate ACE potential for describing the Cu-W immiscible system, but also shed light on understanding the atomistic mechanism during the Cu-W nano-multilayers formation process.