{
  "abstract": "Foundation machine-learning interatomic potentials cover broad configurational and chemical spaces, but their reliability can vary across the atomic environments encountered during a simulation. Here we introduce the calibrated Mahalanobis (CALM) extrapolation grade $γ$, a piecewise differentiable per-atom quantity integrated into GRACE foundation models and evaluated alongside energies and forces in a single model pass. We define $γ$ from nearest-cluster Mahalanobis distances in latent feature space, setting $γ=1$ from the training-distance distribution separately for each element and cluster. Controlled tests show that a normalized random projection of the invariant many-body basis detects structural and chemical extrapolation. On different foundation datasets, OMat24 and SMAX, $γ$ correlates with atomic force errors and separates structures with different error distributions. The CALM grade adds percent-level computational cost, and its spatial gradient guides uncertainty-biased data collection toward configurations with larger absolute force errors.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.40060",
  "author_total": 3,
  "authors": [
    {
      "name": "Yury Lysogorskiy"
    },
    {
      "name": "Anton Bochkarev"
    },
    {
      "name": "Ralf Drautz"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3q5cfqwyxjzsb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.40060",
  "fulltext_url": null,
  "id": "3q5cfqwyxjzsb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.40060",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7215072282",
  "pdf_url": "https://arxiv.org/pdf/2609.40060",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3q5cfqwyxjzsb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:49Z",
      "native_id": "2609.40060",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.40060"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "A latent-space extrapolation grade built into graph atomic cluster expansion foundation potentials",
  "title_text": "A latent-space extrapolation grade built into graph atomic cluster expansion foundation potentials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:49Z",
  "url": "https://research.draconitic.com/paper/3q5cfqwyxjzsb",
  "venue": "arXiv",
  "versions": []
}