  Phononic properties are commonly studied by calculating force constants using
the density functional theory (DFT) simulations. Although DFT simulations offer
accurate estimations of phonon dispersion relations or thermal properties, but
for low-symmetry and nanoporous structures the computational cost quickly
becomes very demanding. Moreover, the computational setups may yield
nonphysical imaginary frequencies in the phonon dispersion curves, impeding the
assessment of phononic properties and the dynamical stability of the considered
system. Here, we compute phonon dispersion relations and examine the dynamical
stability of a large ensemble of novel materials and compositions. We propose a
fast and convenient alternative to DFT simulations which derived from
machine-learning interatomic potentials passively trained over computationally
efficient ab-initio molecular dynamics trajectories. Our results for diverse
two-dimensional (2D) nanomaterials confirm that the proposed computational
strategy can reproduce fundamental thermal properties in close agreement with
those obtained via the DFT approach. The presented method offers a stable,
efficient, and convenient solution for the examination of dynamical stability
and exploring the phononic properties of low-symmetry and porous 2D materials.
