Knowing when to trust machine-learned interatomic potentials

• URL: http://arxiv.org/abs/2605.00640v1
• Date: Fri, 01 May 2026 13:21:56 GMT
• Title: Knowing when to trust machine-learned interatomic potentials
• Abstract summary: We show that PROBE produces a per-prediction reliability probability that tracks actual error without modification to the underlying model.<n> PROBE is post-hoc and architecture-agnostic, and is directly deployable on any machine-learned interatomic potential.
• Score: 1.5586922787941984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These methods scale unfavorably with foundation-scale MLIPs, and their member-disagreement signals correlate weakly with per-molecule prediction error. Here we probe the frozen per-atom representations of a pretrained MLIP with a compact discriminative classifier, recasting MLIP uncertainty quantification as selective classification rather than error regression. The resulting method, PROBE (Post-hoc Reliability frOm Backbone Embeddings), produces a per-prediction reliability probability that monotonically tracks actual error without modification to the underlying model. Across large held-out evaluation sets and two structurally distinct MLIP architectures, PROBE outperforms ensemble disagreement as a binary reliability signal, which strengthens with the expressiveness of the backbone representation, implying a favorable scaling trajectory toward foundation-scale MLIPs. Multi-head self-attention additionally yields per-atom importance maps, providing chemically interpretable diagnostics at no additional computational cost. PROBE is post-hoc and architecture-agnostic, and is directly deployable on any MLIP that exposes per-atom representations.

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