) and training several models, each initiated with a different random seed. The largest deviations (or deviations greater than a certain cutoff) between the models' predictions on the remaining part (98%) of the database can be used to identify and select the problematic structures for expanding the training database. This process is iteratively repeated until the final trained models consistently predict properties of the full database within the desired accuracy. We test this database reduction in section 3.5. faster by more than an order of magnitude, we typically only have access to a few GPUs but can utilize thousands of CPUs. Therefore, the wide range of training runs required for hyperparameter optimization is performed using only CPUs with fewer epochs.