By incorporating orbit information into the MACE training process, we observed significant improvements over the default MACE architecture, which relies solely on atomic types. The tests were run for the graphene with pyridinic-N defect and CsPbI 3 perovskite slab. For example, to achieve a target accuracy of 0.4 kcal/(mol•Å) for the CsPbI 3 slab (sufficient for multiple practical applications), an orbit-based MACE model required approximately 9 × 10 4 parameters-an order of magnitude fewer than the 7 × 10 5 parameters needed for the default MACE model. The advantages of using orbits become more pronounced as system complexity and dataset size increase. Our results consistently demonstrate that orbit-based models outperform standard approaches in force prediction accuracy, owing to their more efficient parameterization.