This work emphasizes the critical role of posthoc calibration in molecular property prediction, improving both the reliability of uncertainty estimates and the efficiency of downstream tasks. Future research could explore incorporating calibration techniques into active learning pipelines for real-time optimization of computational chemistry workflows, such as nonadiabatic dynamics with trajectory surface hopping.