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Knowing when to trust machine-learned interatomic potentials

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called PROBE to assess the reliability of machine-learned interatomic potentials (MLIPs). Unlike existing ensemble-based approaches that scale poorly, PROBE uses a compact classifier on frozen representations from a pretrained MLIP. This post-hoc technique generates a per-prediction reliability probability that accurately tracks actual error without altering the original model. PROBE also provides chemically interpretable per-atom importance maps as a byproduct. AI

IMPACT Introduces a more scalable and accurate method for assessing the trustworthiness of MLIPs, potentially improving their adoption in scientific research.

RANK_REASON The cluster contains an arXiv preprint detailing a new method for evaluating machine-learned interatomic potentials.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Knowing when to trust machine-learned interatomic potentials

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The cluster contains an arXiv preprint detailing a new method for evaluating machine-learned interatomic potentials.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Shams Mehdi, Ilkwon Cho, Olexandr Isayev ·

    Knowing when to trust machine-learned interatomic potentials

    arXiv:2605.00640v1 Announce Type: new 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-disagreem…

  2. arXiv cs.LG TIER_1 English(EN) · Olexandr Isayev ·

    Knowing when to trust machine-learned interatomic potentials

    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 p…

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