EPIC: Differentiable Energy-Force Consistent Uncertainty Quantification and Propagation for Machine-Learned Interatomic Potentials

Machine-learned interatomic potentials (MLIPs) now rival density functional theory in accuracy and are increasingly used to screen vast crystal databases, yet they can produce unreliable predictions for chemistries outside their training distribution without clear warning. Existing force-level evide