{
  "abstract": "Machine learning offers an efficient route for screening metal–organic frameworks (MOFs), but most existing models rely on rigid-framework simulations that neglect framework flexibility. In this work, DFT-derived flexible force fields were used to calculate CO2 sorption and self-diffusion for 5,418 MOFs, providing the training data for machine-learning models capable of rapidly predicting these properties. We investigated different ML algorithms, featurization methods, and the role of physical descriptors in predicting CO2 sorption and self-diffusion in MOFs. For CO2 sorption, a categorical boosting classifier coupled with Stoichiometric-45 featurization method used with scalar descriptors provided predictive accuracy of 76.1%. For CO2 self-diffusion, we tuned a categorical boosting regressor on revised autocorrelation functions coupled with scalar descriptors and achieved moderate predictive accuracy (R2 = 0.69). These results demonstrate that machine learning trained on properties generated from flexible force field simulations can substantially accelerate computational screening while incorporating key effects of framework flexibility, providing an efficient strategy for discovering MOFs for low-pressure CO2 capture. Summary Computational screening of 5,418 metal–organic frameworks using flexible DFT-based force fields captures framework motion effects on CO2 sorption and self-diffusion. Machine learning models were evaluated across descriptors and algorithms. A CatBoost classifier achieved 76.1% sorption accuracy, while an autocorrelation-based regressor predicted self-diffusion (R2 = 0.69), demonstrating the value of flexibility-aware modeling.",
  "abstract_shown": true,
  "affiliations": [
    "Oak Ridge Institute for Science and Education, 100 ORAU Way, Oak Ridge, TN, 37830",
    "Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI, 48109",
    "Leidos Research Support Team, P.O. Box 10940, Pittsburgh, PA, 15236-0940",
    "US DOE, National Energy Technology Laboratory, 626 Cochran Mill Rd, Pittsburgh, PA, 15236",
    "Braskem, 550 Technology Dr, Pittsburgh, PA, 15219",
    "Department of Chemistry",
    "University of Pittsburgh"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Clare B. Martin"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Samir Budhathoki"
    },
    {
      "affs": [
        3,
        5,
        6
      ],
      "name": "Janice A. Steckel",
      "orcid": "0000-0001-9244-3359"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6khmhysgn1fx4.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010813/v1",
  "fulltext_url": null,
  "id": "6khmhysgn1fx4",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010813/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7223179920",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010813/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/6khmhysgn1fx4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010813",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010813/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Flexible force field-based screening and machine learning predictions for CO2 sorption and diffusion in MOFs",
  "title_text": "Flexible force field-based screening and machine learning predictions for CO2 sorption and diffusion in MOFs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/6khmhysgn1fx4",
  "venue": "ChemRxiv",
  "versions": []
}