When Forces Disagree: An Out-of-the-Box Stability and Uncertainty Estimate for Direct-Force Machine Learning Interatomic Potentials

Machine learning interatomic potentials (MLIPs) that predict forces directly, rather than as the gradient of a predicted energy, accelerate atomistic simulations but are unstable in molecular dynamics (MD). They also lack an out-of-the-box single-model uncertainty quantification (UQ) metric, since existing single-model methods require either non-standard MLIP training or post-hoc fitting beyond energy and force matching. Here, we address both problems with the Force Delta (U∆), the magnitude of the difference between the direct and conservative forces of a single model, which is available out of the box for any direct-force MLIP. We first find that MD stability depends more on conservativity than on force accuracy. We then show that U∆ tracks both the exact but expensive Jacobian-based measure of non-conservativity and the resulting instability. Further, U∆ correlates well with the true force error across equivariant and non-equivariant architectures on in- and out-of-distribution systems, rivaling or outperforming other single-model UQ metrics and ensembles without any additional training or fitting beyond standard MLIP training. Finally, finetuning on only 200 structures that maximize U∆ improves both the stability and the accuracy of direct forces in pretrained universal MLIPs. U∆ therefore offers an out-of-the-box way to monitor and improve fast direct-force MLIPs, enabling their safe application to materials discovery.