We investigate atomic force reconstruction heterogeneity using results from the TEA Challenge 2023, revealing how employing orbits -atoms with persistent neighborhood chemical compositions-provides new insights into force prediction errors for the Ac-Phe-Ala5-Lys molecule. This analysis, which yields consistent results across various deep learning architectures and kernel-based ML models, including MACE, SO3krates, and sGDML models, highlights the limitations of classifying atoms solely by their elemental identity. To improve the accuracy and efficiency of MACE, we extend its architecture by explicitly incorporating atomic orbits-groups of atoms identified by permutational symmetry analysis as sharing equivalent chemical environments-as distinct atomic types. As a demonstration, we select two systems: the cesium lead iodide perovskite slab and graphene with pyridinic-N defect. We demonstrate that the proposed symmetry search method notably enhances FF reconstruction for complex systems when combined with the messagepassing graph neural network architecture. This benefits sys-tems where atoms of the same species can be classified into multiple groups with varying neighborhood chemical compositions. For example, for graphene with a pyridinic-N defect, the presence of the defect produces different carbon environments based on proximity to the defect site, while in the CsPbI 3 perovskite slab system, the presence of surfaces and internal layers leads to different atomic environments for the same atomic species.