  Assessments of machine-learned (ML) potentials are an important aspect of the
rapid development of this field. We recently reported an assessment of the
linear-regression permutationally invariant polynomial (PIP) method for
ethanol, using the widely used (revised) MD17 dataset. We demonstrated that the
PIP approach outperformed numerous other methods, e.g., ANI, PhysNet, sGDML,
p-KRR, with respect to precision and notably with respect to speed [Houston
$et$ $al$., $J. Chem. Phys.$ 2022, 156, 044120.]. Here we extend this
assessment to the 21-atom aspirin molecule, using the rMD17 dataset. Both
energies and forces are used for training and the precision of several PIPs is
examined for both. Normal mode frequencies, the methyl torsional potential, and
1d vibrational energies for an OH stretch are presented. Overall, we show that
the PIPs approach outperforms other ML methods, including sGDML, ANI, GAP,
PhysNet, and ACE, as reported by Kov\'acs $et$ $al.$ in $J. Chem. Theory$
$Comput.$ 2021, 17, 7696-7711.
