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

The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials