In previous studies, we have utilised an energy-based selector (further referred to as energy) to determine whether a configuration should be added to the training set. This selector identifies structures that show an error in predicted energy higher than the threshold E T , satisfying the condition E DFT -E MLP > E T . Structures with prediction errors greater than 10 E T were excluded from the dataset as these were too distorted to provide meaningful information. Although reliable, this approach requires QM calculations for each selection step, which is computationally prohibitive for large systems.