{
  "abstract": "Atomic oxygen (AO) erodes polymer surfaces in low Earth orbit, but durability data are limited. We evaluated a regression ensemble trained on 37 MISSE-2 polymers using eight selected bulk properties and five elemental-composition ratios. Its archived leave-one-out R^2(log) was 0.768; this estimate is conditional on feature selection using the complete training table. For nine post-MISSE-2 polymers, the ensemble gave R^2(log) = -0.037 and all predictions were within a factor of two. The training arithmetic-mean baseline also met that tolerance for all nine, highlighting the limited discrimination provided by their narrow target range. Ground-based comparisons included four PLAVIS grades. For an out-of-training mystery polymer with undisclosed composition, the predicted erosion yield was within a factor of 1.92 of the ground-based measurement. Conflicting PLAVIS measurement records prevent a definitive residue-erosion ordering or ground-validation score. Thermal residue, SEM contrast and surface chemistry provide distinct observational evidence; they do not establish a protective-layer mechanism. The results support further evaluation of orbital-data screening models with reconciled measurement records and prospectively fixed inputs..",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.07547",
  "author_total": 4,
  "authors": [
    {
      "name": "Youngwoo Choi"
    },
    {
      "name": "Doeun Koo"
    },
    {
      "name": "Seungbum Hong"
    },
    {
      "name": "Hyun Jung Kim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3nkq0ds8fdcrk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.07547",
  "fulltext_url": null,
  "id": "3nkq0ds8fdcrk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.07547",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "physics.space-ph"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7221048038",
  "pdf_url": "https://arxiv.org/pdf/2610.07547",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3nkq0ds8fdcrk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:22Z",
      "native_id": "2610.07547",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.07547"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:physics.space-ph",
      "subfield": "stellar"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Data-driven prediction of atomic-oxygen erosion yield in polymers from bulk physical properties",
  "title_text": "Data-driven prediction of atomic-oxygen erosion yield in polymers from bulk physical properties",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:22Z",
  "url": "https://research.draconitic.com/paper/3nkq0ds8fdcrk",
  "venue": "arXiv",
  "versions": []
}