  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.
