In MPNN models, the increase in atom types correlates with a rise in trainable parameters. However, our findings demonstrate that the observed improvement of MACE models within the proposed approach is more than just a result of an increased number of parameters. Figure (a) illustrates the accuracy of these models as a function of the number of channels, which represent the dimensionality of the internal representation at a specific symmetry level. Each model type and channel count underwent training and testing on four distinct datasets, with the resulting variability in accuracy depicted as error bars. Graphs for all model types exhibit linear trends in the logarithmic scale, where the models treating all atoms as the same species have the lowest accuracy. In contrast, orbit models display the highest accuracy for the same number of channels. Notably, the difference in accuracy among these models remained nearly constant when plotted on a logarith-mic scale. This suggests that increasing the number of parameters for a given tensor representation can provide sufficient capacity to capture the neighborhood chemical composition, even without considering specific atom types. This is particularly true in systems where the neighborhood chemical composition remains consistent during the system's evolution.