Derivative-Aware Uncertainty Quantification for Equivariant MLIPs

Uncertainty quantification (UQ) for machine-learned interatomic potentials (MLIPs) is essential considering potential system distribution shift and data scarcity. It faces unique challenges as MLIPs have to respect physics principles: energies must respect symmetry, forces are derivatives of energy, and posterior samples should ideally remain physically valid potentials. We propose derivative-aware subnetwork Laplace, a post-hoc Bayesian UQ framework for equivariant MLIPs. Starting from a pre-trained deterministic model, we select to Bayesianize a compact force-informed subnetwork by learning a Gaussian posterior which incorporates both energy and force Jacobians. The resulting global posterior induces coherent sampled energy surfaces and force fields, while preserving invariance, equivariance, and conservative structure when applied within an admissible equivariant parameterization. We evaluate DASL on controlled and molecular benchmarks under distribution shift, showing that derivative-aware posterior geometry improves force uncertainty without retraining or ensemble.