Several strategies have been developed for UQ, spanning from rigorous and computationally extremely expensive Bayesian estimates to pragmatic ensemble-of-models training . However, many such estimates have been shown to be overconfident when test data are drawn far from the sampling distribution of the training data . This limitation represents a serious drawback for the overall reliability of ML models in atomistic simulations, where they promise to deliver first-principles quality results.