{
  "abstract": "Accurate reproduction of electronic-structure labels by machine-learning interatomic potentials does not necessarily ensure reliable condensed-phase properties. We investigate the effects of basis-set superposition error (BSSE) and an explicit Axilrod-Teller-Muto (ATM) three-body dispersion contribution on molecular-liquid predictions. Starting from MACELES-OFF, we fine-tune a baseline potential at the $ω$B97M-V/def2-TZVPD level. A separate branch learns counterpoise (CP) energy and force corrections with the baseline frozen, while a D4-parametrized ATM term is added during molecular-dynamics inference. Mean atom-normalized CP and ATM contributions are 0.222 and 0.205 meV/atom, respectively, for the analyzed SPICE2 configurations from OMol25. We compare baseline, CP-only, ATM-only, and CP + ATM variants for 39 organic liquids. Relative to baseline, the mean system-wise density changes are -1.17%, -10.38%, and -11.93% for CP-only, ATM-only, and CP + ATM, respectively; the corresponding enthalpy-of-vaporization changes are -2.79%, -14.74%, and -18.32%. For the 11 liquids with experimental self-diffusion data, the mean self-diffusion changes are +17.66%, +203.02%, and +230.36%. Both corrections thus lower density and enthalpy of vaporization while increasing molecular mobility on average. ATM dominates these changes, while CP provides a smaller contribution; their combination yields the largest average shifts. These results show that corrections with sub-meV/atom mean values on the analyzed configurations can substantially reshape the predicted structural, thermodynamic, and transport properties of molecular liquids.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.06109",
  "author_total": 8,
  "authors": [
    {
      "name": "Haihui Wang"
    },
    {
      "name": "Xinyan Wang"
    },
    {
      "name": "Jiashu Liang"
    },
    {
      "name": "Yuanheng Wang"
    },
    {
      "name": "Chang Han"
    },
    {
      "name": "Wen Yan"
    },
    {
      "name": "Tianze Zheng"
    },
    {
      "name": "Kuang Yu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/anhq3qqknk21f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.06109",
  "fulltext_url": null,
  "id": "anhq3qqknk21f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.06109",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.chem-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.06109",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/anhq3qqknk21f.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T15:20:56Z",
      "native_id": "2611.06109",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.06109"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Subtle Energy Corrections Reshape Condensed-Phase Property Predictions from a Machine-Learning Interatomic Potential",
  "title_text": "Subtle Energy Corrections Reshape Condensed-Phase Property Predictions from a Machine-Learning Interatomic Potential",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:19Z",
  "url": "https://research.draconitic.com/paper/anhq3qqknk21f",
  "venue": "arXiv",
  "versions": []
}