{
  "abstract": "The energetic effect of a force error depends on atomic motion. We establish a directional residual-work coefficient combining directional curvature mismatch with the spatial distribution of the residual response. For conservative potentials force-matched at an anchor, it determines the leading signed work at the first crossing of a small force-error budget. At a 474-atom lithium-electrolyte interface, predictions fixed before future reference evaluations differ from measurements by less than 4.6% of predicted work across 24 prescribed endpoints. Changing only the initial velocity direction at fixed structure and initial total kinetic energy reverses the force-work ranking. At the same admitted time of 0.25 fs, one direction gives an 11.6% larger maximum force residual but 36.1% less work. The reversal recurs at a second structure. The framework connects force tolerances to reference-energy transfer, providing a physical basis for potential assessment and adaptive reference allocation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09251",
  "author_total": 8,
  "authors": [
    {
      "name": "Peng Kang"
    },
    {
      "name": "Da Wan"
    },
    {
      "name": "Shulin Bai"
    },
    {
      "name": "Vincent Michaud-Rioux"
    },
    {
      "name": "Zhen Li"
    },
    {
      "name": "Yu Liu"
    },
    {
      "name": "Lei Zheng"
    },
    {
      "name": "Li-Dong Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cwhgjm0358b7d.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09251",
  "fulltext_url": null,
  "id": "cwhgjm0358b7d",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09251",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7212118573",
  "pdf_url": "https://arxiv.org/pdf/2609.09251",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cwhgjm0358b7d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:12Z",
      "native_id": "2609.09251",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09251"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "The energetics of force errors in machine-learned molecular dynamics",
  "title_text": "The energetics of force errors in machine-learned molecular dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:12Z",
  "url": "https://research.draconitic.com/paper/cwhgjm0358b7d",
  "venue": "arXiv",
  "versions": []
}