Throughout the paper, we evaluate data aggregation by comparing the performance of different ML estimators before and after increasing the dataset size. These models include baselines and SOTA for chemical properties prediction given the stoichiometry, with representatives of both classical and Deep Learning (DL) approaches. In more detail, we consider ridge regression as a simple baseline model, random forest regression as a robust model for low-data regimes , Roost [Goodall and ] as a DL model based on graph representations and CrabNet as a transformer-based approach and representative of the SOTA. Performance is assessed through the ordinary procedure of train-test split and on the mean absolute error (MAE), a typical metric used for regression that quantifies the absolute deviation between models' predictions and true corresponding values.