After training the initial ensemble of models, we repeated the sampling outlined above for generating off-equilibrium and reactive conformations using the current AIMNet2-Pd potential. We performed 4 iterations of active learning based on query-by-committee disagreement of predicted with energy and force criteria of 0.1 eV and 1.0 eV/AÅ , respectively. Following active learning, the final dataset contained 1.38M samples, which were then used to train the ensemble of production-quality AIMNet2-Pd models.