Several studies have proposed methods to evaluate and validate calibration in regression models. Kuleshov et al. introduced a calibration curve that assesses how well the predictive distribution covers the observed errors, averaged across the dataset. Levi et al. later suggested using a reliability diagram, where uncertainties and expected errors are checked for correlation within bins of increasing uncertainty. Recently, Pernot identified limitations in these approaches and proposed an additional analysis of z-scores for variance-based UQ methods. Although UQ and calibration for molecular property prediction and interatomic ML potentials have been increasingly studied, the training and inference methods often do not inherently guarantee good calibration. In this study, we focused on two distinct datasets, the QM9 and WS22, and tailored UQ approaches to their specific characteristics.