Our approach combines deep ensembles for uncertainty quantification with a Query -By-Committee (QBC) strategy for active learning. The prediction variance of the ensemble quantifies epistemic uncertainty, guiding data selection and iteratively improving model generalization. The predictive uncertainties were then evaluated on the geodesic interpolation points, representing a distribution shift, which is a realistic scenario for active learning applications, particularly surface hopping dynamics. We tuned 3 hyperparameters (learning rate, activation function, and number of neurons) to create the ensembles, generating 8 ANI models. All models achieved accuracy within 1 kcal/mol, meeting the threshold for subchemical accuracy.. The variation of the models and their corresponding performance for energy and forces are listed in Table . While the other hyperparameters are mentioned in Table . Figure shows a 2D projection of the WS22 dataset onto the first two principal components (PC1 and PC2), with points colored by the log of the predicted total uncertainty (σ total ) in kcal/mol.