{
  "abstract": "The integrated crystal orbital Hamilton population (ICOHP) is the standard measure of bond strength in a DFT calculation, but every structure needs its own DFT single point and LOBSTER projection, which takes days for a cell of a hundred atoms, such as a grain boundary or a catalytic nanoparticle. We predict ICOHP from the frozen descriptors of a MACE foundation model. A symmetric pair head reads the scalar and bond-projected vector channels of the two atoms and corrects a pair baseline built from the element embeddings of the same model. We tested the model on the high-entropy diboride (Hf,Zr,Ti,Nb,Ta)B$_2$, Pt clusters on defective graphene and Pt/Pd nanoparticles with adsorbed O$_2$. The error on bonds stronger than 0.5 eV is 1.6-4.1%, about half that of the Gaussian-process model of Ferreira et al. trained on the same data. Trained only on high-temperature HEB$_2$ snapshots, the model reproduces the LOBSTER ICOHP of relaxed grain boundary cells to 0.06 eV in about a second, against 5-51 hours of LOBSTER. The vector channels halve the error compared to the scalar ones, and the baseline keeps the model usable with two training structures. With geometries from a fine-tuned MACE potential, the model gives the bonding of Pt/Pd particles over a continuous O$_2$ coverage, and DFT on these particles shows that O$_2$ costs a Pt-rich shell several times more metal--metal bonding than a Pd shell.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.40445",
  "author_total": 3,
  "authors": [
    {
      "name": "Daniil S. Alekseev"
    },
    {
      "name": "Ilya V. Chepkasov"
    },
    {
      "name": "Alexander G. Kvashnin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6eck7xfhb6dxt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.40445",
  "fulltext_url": null,
  "id": "6eck7xfhb6dxt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.40445",
  "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"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.40445",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6eck7xfhb6dxt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:28Z",
      "native_id": "2610.40445",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.40445"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "ML-COHP: predicting bond descriptors using latent space of a universal interatomic potentials",
  "title_text": "ML-COHP: predicting bond descriptors using latent space of a universal interatomic potentials",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:47Z",
  "url": "https://research.draconitic.com/paper/6eck7xfhb6dxt",
  "venue": "arXiv",
  "versions": []
}