To further explore how these equivalence classes influence model performance, we assessed the impact of different orbitbased structural encodings on MACE's performance by constructing three datasets containing 200, 600, and 1,000 configurations each. For every dataset, we trained three types of models using standard hyperparameter settings: one with default atomic species (formally classified as a 0NN model) and two incorporating 1NN and 1+2NN structural encodings. Training process followed a 50/50 split between training and validation datasets.