Beyond providing insight into the origin of force reconstruction heterogeneity observed across all ML models and chemical systems analyzed, orbits can also be directly leveraged to improve the performance of state-of-the-art MLFF architectures. As a demonstration, we considered a 15 × 15 graphene lattice containing a pyridinic-N defect, shown in Figure (a), which disrupts the symmetry among carbon atoms. For this study, we choose the MACE model, an equivariant MPNN, and employ orbits (atoms with persistent neighborhood chemical compositions) extracted using the developed graph-based symmetry search algorithm to enhance training efficiency and predictive performance. The standard MACE architecture has three hyperparameters that are critical for accuracy and ultimately define the model's size: