{
  "abstract": "The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model for a given application. We benchmark 15 pretrained MLIPs, evaluating each one on accuracy, speed, memory use, and ability to produce stable simulations. This provides an objective basis for practitioners to select the most appropriate MLIP for their own simulations and offers insight into which factors most strongly influence model accuracy. We find that the number of model parameters and the size of the training set are both strongly correlated with accuracy, but observe no benefit from including explicit Coulomb energy terms. Speed and memory use are determined as much by the model architecture as by the size of the model.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry",
    "Stanford University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Peter K. Eastman",
      "orcid": "0000-0002-9566-9684"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Evan Pretti",
      "orcid": "0000-0001-9487-7185"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Thomas E. Markland",
      "orcid": "0000-0002-2747-0518"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/91ksdqv1fzq5a.bib",
  "cited_by": 8,
  "doi": "10.1021/acs.jctc.6c00130",
  "fulltext_url": null,
  "id": "91ksdqv1fzq5a",
  "issns": [
    "1549-9618",
    "1549-9626"
  ],
  "landing_url": "https://doi.org/10.1021/acs.jctc.6c00130",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7163707869",
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.6c00130?ref=article_openPDF",
  "pmcid": "PMC13296480",
  "pmid": "42247474",
  "published": "2026-06-05",
  "publisher": "American Chemical Society",
  "ris": "https://research.draconitic.com/paper/91ksdqv1fzq5a.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:19Z",
      "native_id": "W7163707869",
      "source": "openalex",
      "url": "https://doi.org/10.1021/acs.jctc.6c00130"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations",
  "title_text": "Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:22Z",
  "url": "https://research.draconitic.com/paper/91ksdqv1fzq5a",
  "venue": "Journal of Chemical Theory and Computation",
  "versions": []
}