In this study, we investigated the role of posthoc calibration in improving the reliability and utility of predictive uncertainties in molecular machine learning models, focusing on energy and force predictions. Using the QM9 dataset and the challenging WS22 dataset, we demonstrated the performance of the EGNN-DER model and deep ensembles and the effectiveness of various posthoc calibration techniques, including isotonic regression (ISR), standard scaling, and GP-Normal.