  Here we assess two machine-learned potentials, one using the symmetric
gradient domain machine learning (sGDML) method and one based on
permutationally invariant polynomials (PIPs). These are successors to a PIP
potential energy surface (PES) reported in 2004. We describe the details of
both fitting methods and then compare the two PESs with respect to precision,
properties, and speed of evaluation. While the precision of the potentials is
similar, the PIP PES is much faster to evaluate for energies and energies plus
gradient than the sGDML one. Diffusion Monte Carlo calculations of the ground
vibrational state, using both potentials, produce similar large anharmonic
downshift of the zero-point energy compared to the harmonic approximation the
PIP and sGDML potentials. The computational time for these calculations using
the sGDML PES is roughly 300 times greater than using the PIP one.
