AIMNet2-Pd models were trained using the RAdam optimizer for ~1M steps with a learning rate annealing from 4 x 10 -4 to 4 x 10 -5 according to a reduce on plateau scheduler. Each step consisted of sampling a batch size of 600 chemical systems using a distributed data parallel (DDP) framework on 4 A40 GPUs. The loss function was defined via a sum of the total energy meansquared-error (MSE) normalized by square root of the number of atoms in the sample, MSE on components of atomic forces, MSE on molecular dipole and quadrupole moments with weights of 0.79, 0.16, 0.040, and 0.016, respectively. The functional form of the loss function is consistent with the original AIMNet2 report. It should be noted that the weighting factors correspond to typical AIMNet2 training units of eV, AÅ , and electron charge.