In chemical applications, ensemble methods where multiple independently trained models are used to estimate quantities of interest, such as an observation's mean and variance, are widely used to measure the uncertainty. In the context of molecular potentials, uncertainties can arise from various sources, including the quality of training labels (e.g., inaccuracies in reference quantum chemistry calculations) and the representativeness of the sampled geometries in capturin g the underlying chemical landscape. Kellner and Ceriotti recently proposed a cost-effective, accurate deep-learning framework using model ensembles with shared weights up to the final layers, offering a simple and reliable way to propagate uncertainty in derived quantities. As ML models expand across various fields, new UQ techniques with potential for chemical applications have emerged, spanning frequentist and Bayesian approaches. Bayesian neural networks (NNs), represent a key advancement in uncertainty quantification for chemical applications, , predicting probability distributions over network parameters to quantify uncertainty. Similarly, Monte Carlo Dropouts estimate predictive distributions through multiple stochastic forward passes, providing a practical approximation to Bayesian inference. Gaussian process regression is a Bayesian method that naturally incorporates uncertainty. However, it is computationally expensive, limiting its use with large datasets. On the other hand, single deterministic networks, such as those based on evidential learning, represent a promising frequentist approach. By learning parameters of a higher-order distribution over likelihood parameters, they require only one model, making variance estimation more computationally efficient. This diverse suite of techniques highlights the evolving landscape of UQ methods, each with unique strengths and limitations suited to specific applications. However, calibration is an essential aspect of predictive uncertainty. It ensures alignment between predicted uncertainties and actual empirical errors. In practice, uncertainties predicted by either a single model or an ensemble often lack proper calibration, necessitating posthoc recalibration techniques to improve reliability. Calibration assesses whether the model's predicted uncertainty reliably reflects the observed errors, ensuring that predictions with low uncertainty correspond to smaller errors and vice versa. This alignment is critical for confidence in data -driven workflows. In evaluating calibration, the asymmetry between errors and uncertainties must be considered. By convention, errors are often assumed to follow a distribution (typically Gaussian), meaning small uncertainties should correspond to small errors, while large errors should align with large uncertainties. However, large uncertainties may also coincide with small errors, so there is no direct correlation between uncertainty levels and error magnitude. Instead, there should be a correlation between uncertainties and the expected error magnitude.