We report a study on several static and dynamic properties of the early trivalent liquid rare-earth metals
at thermodynamic conditions near their respective melting points. It has been performed by resorting to
machine learning (ML) techniques, in which the associated neural network-based interatomic potentials
were derived from ab initio molecular dynamics simulations within Hubbard-corrected density functional
theory. We report the results obtained for the static structural properties, including an analysis of the
local short-range order. Single-particle and collective dynamic properties have also been obtained, from
which transport coefficients and wavevector-dependent dispersion relations are evaluated. The results
show a quite homogeneous behavior of the structural, dynamic, and transport properties throughout the
series. The electronic properties have been obtained from the ab initio simulations, and show important
discrepancies with respect to the low temperature solids, portraying a more band-like picture of the 4f
states in the liquid.
