For geometry-varying datasets like WS22, where higher precision is essential, we used an ensemble of ANI models to estimate the uncertainty in predicted energy and forces for acrolein. The ensemble approach was chosen because the mean prediction from multip le models provides greater accuracy, making it a more reliable choice for machine learning potentials. Moreover, the DER approach is not well-suited for derived quantities such as forces, as it does not explicitly account for force consistency derived from energy gradients. In such cases, ensemble-based methods offer a more robust framework for uncertainty quantification. Additionally, for WS22, uncertainty quantification was extended to out-of-distribution data, ensuring robustness beyond the training domain.