Traditional MLFFs typically rely on full symmetry groups to model atomic environments. However, recent advances suggest that strategically restricting these symmetry groups can simplify learning tasks. For example, atom embeddings with restricted permutation symmetry, derived from fully permutation-invariant neural network representations (e.g., SchNet, PaiNN), have been shown to improve prediction accuracy. In modern MLFFs, atoms of the same chemical element are typically treated as identical, permitting intricate structural and chemical transformations. However, in complex systems, atoms of the same element often participate in various interaction patterns, such as forming single and double bonds with neighboring atoms of differing types. In these cases, restricting the full symmetry group by defining identical particles based on shared chemical neighborhoods may be advantageous, thus simplifying the learning task for ML models. This approach aligns with strategies used for decades in empirical force fields, where, for example, the bond order of carbon atoms determines their interaction parameters. However, to achieve broad applicability, it is crucial to go beyond identifying symmetries tied to specific chemical compositions and geometries. Instead, symmetries should be defined across the entire relevant chemical and configurational space.