Bayesian Last Layer for Neural Force Fields

Reliable uncertainty quantification is essential for deploying Machine Learning Interatomic Potentials (MLIPs), also known as Neural Force Fields, especially when molecular dynamics or materials simulations encounter configurations outside the training distribution. Deep ensembles remain the strongest practical baseline 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. BLL is known to underestimate the uncertainties. We provide an in-depth analysis that shows two sources of miscalibration and introduce a simple post-hoc recalibration to address the issue. On MPtrj and rMD17 benchmarks, including both in-distribution tests and increasingly out-of-distribution regimes, BLLs that are recalibrated on in-distribution examples produce uncertainty estimates competitive with ensembles, while using only one base model.