  The polynomial machine learning potentials (MLPs) described by polynomial
rotational invariants have been systematically developed for various systems
and used in diverse applications in crystalline states. In this study, we
systematically investigate the predictive power of the polynomial MLPs for
liquid structural properties in 22 elemental systems with diverse chemical
bonding properties, including those showing anomalous melting behavior, such as
Si, Ge, and Bi. We compare liquid structural properties obtained from molecular
dynamics simulations using the density functional theory (DFT) calculation, the
polynomial MLPs, and other interatomic potentials in the literature. The
current results demonstrate that the polynomial MLPs consistently exhibit high
predictive power for liquid structural properties with the same accuracy as
that of typical DFT calculations.
