  There has been great progress in developing methods for machine-learned
potential energy surfaces. There have also been important assessments of these
methods by comparing so-called learning curves on datasets of electronic
energies and forces, notably the MD17 database. The dataset for each molecule
in this database generally consists of tens of thousands of energies and forces
obtained from DFT direct dynamics at 500 K. We contrast the datasets from this
database for three "small" molecules, ethanol, malonaldehyde, and glycine, with
datasets we have generated with specific targets for the PESs in mind: a
rigorous calculation of the zero-point energy and wavefunction, the tunneling
splitting in malonaldehyde and in the case of glycine a description of all
eight low-lying conformers. We found that the MD17 datasets are too limited for
these targets. We also examine recent datasets for several PESs that describe
small-molecule but complex chemical reactions. Finally, we introduce a new
database, "QM-22", which contains datasets of molecules ranging from 4 to 15
atoms that extend to high energies and a large span of configurations.
