When compared to ANI-2x on the COMP6 benchmark, MACE has lower energy errors on the "DrugBank" subset and lower force component errors on all of the subsets. This is particularly significant because in addition to closed-shell molecules which may be evaluated with ANI-2x, MACE is applicable to a much larger space of sp The learning curve of MACE as evaluated on the different test sets is in Figure . We see that while the curve seems to be relatively saturated for the ZINC test set, the model's extrapolation, as measured on the other test sets, is still improving with additional structures.