Figure presents a molecular snapshot highlighting four sets of carbon atoms that form orbits with identical neighborhood chemical compositions up to the second nearest neighbors, considering only orbits containing multiple atoms. Table II reports the mean absolute errors (MAEs) for atomic force reconstruction using two message-passing neural network (MPNN) architectures, MACE and SO3krates, along with the kernel-based sGDML model. To account for variations in force magnitudes across the molecule, MAEs are expressed as percentages of the averaged norm of the true force acting on an atom. Carbon atoms within the same orbit are grouped together, revealing a clear pattern: relative atomic force MAEs remain highly consistent within each orbit but differ significantly across different orbits. Similar results can be observed for absolute atomic force MAE values, as well as for other chemical elements and systems benchmarked in the TEA Challenge 2023.