Multi-Fidelity Transfer Learning Reshapes Uncertainty Quantification in Atomistic Models

Multi-fidelity training is an increasingly important form of transfer learning in atomistic machine learning, where models pretrained on abundant low-fidelity calculations are adapted using scarce high-fidelity labels. Although uncertainty quantification is critical for deploying these models beyond their training distribution, it remains unclear how predictive uncertainty and its decomposition change during transfer across fidelity levels. Here we extend the MACE architecture with mean–variance outputs and use deep ensembles to study aleatoric and epistemic uncertainty across single-fidelity training and low-to-high fidelity fine-tuning under system-level distribution shift. Our results show that the transfer through high-fidelity fine-tuning not only improves accuracy but also produces more stable and reliable uncertainty estimates. Furthermore, it changes the role of uncertainty decomposition and particularly which components are informative for generalization. Aleatoric uncertainty often loses calibration and ranking power under changes in fidelity and distribution shift, whereas epistemic uncertainty becomes a more reliable indicator of errors in unseen systems. These findings show that the behavior and quality of uncertainty estimates in atomistic models are not fixed properties of the model architecture, but depend on the training pathway.