When Forces Disagree: A Data-Free Fast Uncertainty Estimate for Direct-Force Pre-trained Neural Network Potentials

Neural Network Interatomic Potentials (NNIPs) are a cornerstone of modern atomistic simulations, but their reliability is limited by the difficulty in quantifying prediction uncertainty.Current uncertainty quantification (UQ) methods present a trade-off: model ensembles offer a robust, data-free metric based on model disagreement but are computationally expensive, while faster single-model methods typically require access to the original training data which can be practically inconvenient and chemically sparse.This paper introduces a novel differentiable UQ metric for direct-force pre-trained models that combines the strengths of both paradigms, offering the data-free reliability of ensembles with the computational speed of a single model.Our metric is derived from the internal disagreement between two force predictions from a single NNIP—the directly predicted (non-conservative) force and the energy-gradient-derived (conservative) force.We show a strong monotonic correlation between this force disagreement and the true force error against Density Functional Theory calculations.This relationship is robust across a diverse set of materials and holds even for out-of-distribution structures generated via adversarial attacks.Because the method is computationally cheap and requires no training data, it offers a powerful, out-of-the-box tool for on-the-fly assessment of model confidence with wide-ranging applications for reliable atomistic modeling.