To further explore the influence of orbits on atomic force reconstruction across various chemical systems and MLFF architectures, we analyze the results from the TEA Challenge 2023. The TEA Challenge 2023 is a benchmarking initiative that evaluates MLFF performance across diverse molecular systems, systematically assessing models with various architectures such as MACE, SO3krates, sGDML, SOAP/GAP, and FCHL19 * . The test systems included two biomolecular structures (alanine tetrapeptide and N-acetylphenylalanyl-pentaalanyl-lysine), a 1,8naphthyridine/graphene interface, and a methylammonium lead iodide perovskite. The challenge focused on key tasks, FIG. . A snapshot of the Ac-Phe-Ala5-Lys molecule geometry. C -C represent sets of carbon atoms belonging to four different orbits. including the reconstruction of potential energy surfaces, the handling of incomplete reference data, the modeling of multicomponent systems, and the simulation of complex periodic structures. The results revealed pronounced heterogeneity in atomic force prediction errors, with worst-to-best atomistic MAE ratios ranging from 5:1 to 6:1, consistently observed across different architectures. Here, we selected the Ac-Phe-Ala5-Lys molecule (100 atoms) to investigate the origin of this heterogeneity using orbits identified through the developed permutational symmetry search method. Since orbits are defined as sets of vertices that remain indistinguishable under all graph automorphisms, applying graph automorphism analysis directly to complex molecules can lead to an excessive number of chemically distinct environments, even for atoms that should be treated identically based on chemical intuition. Distinguishing atomic species based on their local neighborhood chemical compositions is more ef-fective than relying solely on global permutational symmetry in such systems. Therefore, while orbits traditionally refer to global permutations, hereafter, we extend the term to include atoms characterized by distinct local environments.