To assess the impact of orbits on data efficiency and model accuracy, we assembled three DFT-based datasets, comprising 600, 1,000, and 2,000 configurations each. Subsequently, two MACE models with standard hyperparameter settings were trained for each dataset -one utilizing default Force MAE distributions calculated for all atoms in the system, for the MACE models trained on 100, 300, and 500 reference geometries. (e, f) Force MAE distributions specifically for nitrogen, carbons bonded to nitrogen, and other carbon atoms in the system using MACE models trained on 500 configurations, where default MACE architecture compared with (e) the first and second-neighbor-based MACE model, and with (f) the first-neighbor-based MACE model. atomic species and the other incorporating orbits. The training process used a 50/50 split for training and validation subsets. For experiments on model size, four distinct models were trained for each model type and channel count using a crossvalidation procedure on 2,000 configurations, with a 50/50 split between training and validation datasets. All obtained ML models were tested on the test sets consisting of 8,000 configurations, ensuring the stability and reliability of our results.