Bayesian Linear Last Layer for Machine Learning Interatomic Potentials

Reliable uncertainty quantification is essential for deploying Machine Learning Interatomic Potentials (MLIPs), especially when molecular dynamics or materials simulations encounter configurations outside the training distribution. Deep ensembles are among the strongest practical baselines for MLIP uncertainty, but training and storing several copies of a modern pretrained model is often prohibitively expensive. We show that Bayesian Linear Last Layers (BLLs) provide a scalable alternative for MLIPs: a single pretrained backbone supplies atomic features, while exact Bayesian inference over the final force-prediction layer gives predictive uncertainties; the construction extends directly to equivariant force heads. However, BLLs are known to be overconfident, so we analyze the sources of this miscalibration and introduce a simple post-hoc recalibration to address the issue. We show that BLLs recalibrated on in-distribution examples are competitive with LoRA ensembles on several foundation backbones for molecules and materials.