A key contribution of this work is the application of the deep ensemble method to decompose total predictive uncertainty into its aleatoric and epistemic components within an atom classification framework. This decomposition allows for a more granular understanding of model confidence, enabling end users to identify unreliable predictions and make more informed decisions. For model developers and data curators, the distinction between aleatoric and epistemic uncertainty provides actionable insights for optimizing data collection and model refinement.