ML potentials discussed here are trained on small data sets of only 1000 structures to showcase GDML data efficiency. Training sets were determined through an iterative training procedure to reduce the maximum model error. GAP and SchNet models were trained on the same training sets as GDML for a fair comparison. In theory, training sets could be tailored for GAP and SchNet to reduce errors; however, a cursory attempt did not substantially improve results. We reiterate that GAP and SchNet normally require substantially large training sets. In other words, GAP and SchNet potentials presented here are technically underfitted compared to standard practices. More information can be found in the ESI.