To put this performance into perspective, the most exhaustive GemNet model yields a force MAE of 0.0043 eV Å-1 , which can be compared with an MAE of 0.015 eV Å-1 for the recently developed SpookyNet 23 architecture (in this case trained on 4.2Mio molecules). In addition, GemNet outperforms SchNet and DimNet++ 25 on QM7-x for nearly all points of the learning curve (with the only exception being the AE error of the 32k model). Importantly, the energy errors are very low (0.01 eV = 0.23 kcal mol -1 ) despite the low weighting of AEs in the loss. It is furthermore notable that even the model trained on 3.2k configurations displays quite good performance with MAEs of around 0.035 eV Å-1 and 0.05 eV (≈ 1 kcal mol -1 ).