The equivariant message-passing neural network MACE was used as provided, with hyperparameters as indicated in the following paragraphs. In all cases, training was performed using the AMSGrad optimizer with hyperparameters and learning rate schedules given by the defaults set in the MACE package. All models were constructed using two layers with 128 channels for even and odd parity features and a maximum order l max = 3 of spherical harmonics. Eight Bessel basis functions and a polynomial cutoff function of order p = 5 were used for generating the radial features, which are passed into MLPs with three layers of 64 nodes each and SiLU serving as the non-linear transfer function. The final readout function generating the atomic energies is given by a MLP with a single layer of 16 hidden features. In the Sup-porting Information, we furthermore report results with smaller MACE models (differing in the number of channels and cutoff radius), as well as invariant NeuralIL 2,31 models.