Our results show that while increasing the number of trainable parameters in MPNN models generally improves prediction accuracy, the proposed orbit-based approach achieves comparable or even superior accuracy with significantly fewer parameters. This suggests that explicitly incorporating chemically distinct atomic environments through orbits enables a more efficient parameterization, reducing model complexity without sacrificing accuracy.