  Combining first-principles accuracy and empirical-potential efficiency for
the description of the potential energy surface (PES) is the philosopher's
stone for unraveling the nature of matter via atomistic simulation. This has
been particularly challenging for multi-component alloy systems due to the
complex and non-linear nature of the associated PES. In this work, we develop
an accurate PES model for the Al-Cu-Mg system by employing Deep Potential (DP),
a neural network based representation of the PES, and DP Generator (DP-GEN), a
concurrent-learning scheme that generates a compact set of ab initio data for
training. The resulting DP model gives predictions consistent with
first-principles calculations for various binary and ternary systems on their
fundamental energetic and mechanical properties, including formation energy,
equilibrium volume, equation of state, interstitial energy, vacancy and surface
formation energy, as well as elastic moduli. Extensive benchmark shows that the
DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg
system within the full range of concentration.
