No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials

Assessments of machine-learning (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) rMD17 data
set. We demonstrated that the PIP approach outperformed numerous other
methods, e.g., ANI, PhysNet, sGDML, and 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 data set,
with a focus on the speed of evaluation. 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. We show
that the PIP approach achieves the level of precision obtained from
other ML methods, e.g., atom-centered neural network methods, linear
regression ACE, and kernel methods, as reported by Kovács et
al. in J. Chem. Theory Comput. 2021,
17, 7696–7711. More significantly, we show that the PIP PESs
run much faster than all other ML methods, whose timings were evaluated
in that paper. We also show that the PIP PES extrapolates well enough
to describe several internal motions of aspirin, including an OH stretch.