  The use of machine learning interatomic potentials (MLIPs) in simulations of
materials is a state-of-the-art approach, which allows achieving nearly
\textit{ab initio} accuracy with orders of magnitude less computational cost.
Multicomponent disordered systems have a highly complicated potential energy
surface due to both topological and compositional disorder. That arises issues
in MLIPs developing, such as optimal design strategy of potentials and their
predictability and transferability. Here we address MLIPs for multicomponent
metallic melts taking the ternary Al-Cu-Ni ones as a convenient example. We use
many-body deep machine learning potentials as implemented in the DeePMD-kit to
build MLIP that allows describing both atomic structure and dynamics of the
system in the whole composition range. Doing that we consider different sets of
neural networks hyperparameters and learning schemes to create an optimal MLIP,
which allows archiving good accuracy in comparison with both \textit{ab initio}
and experimental data. We find that developed MLIP demonstrates good
compositional transferability, which extends far beyond compositional
fluctuations in the training configurations. The results obtained open up
prospects for simulating structural and dynamical properties of multicomponent
metallic alloys with MLIPs.
