{
  "abstract": "Electro-optic materials, whose refractive index changes linearly with an applied electric field (the Pockels effect), set the speed and energy of optical modulators and microwave-to-optical links for quantum computing. Designing better materials requires optimizing composition alongside temperature, strain, and bias field. However, general repositories lack electro-optic tensor data. We present a pipeline that builds such a database from literature following the rule: \"no value is better than a wrong value.\" The pipeline has five stages: physics-aware acquisition, layout-aware extraction via LLM and deterministic table parsers, a three-tier verification gate, cross-paper consensus using median and median absolute deviation, and a noise-robust surrogate model audited by leave-one-material-out (LOMO) testing. Physics enters directly: notation is mapped, point-group symmetry-forbidden Pockels components are rejected, equivalent labels are merged, and values carry provenance tags. In a run over 49 publications, the gate processed 816 candidates into 414 accepted, 215 held for review, and 187 rejected. Against a hand-certified gold standard of 303 values from 16 papers, the run achieved a precision of 0.51 and recall of 0.48 for exact record matches. Across four database snapshots, held-out materials where the surrogate model beat a predict-the-mean baseline grew from 1 of 4 to 9 of 13 as the database expanded from 10 to 22 materials. While not a controlled comparison, this trend supports the premise that a precision-oriented, physics-checked database can power forward models beyond its training set and clarifies requirements for inverse material design.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.34628",
  "author_total": 1,
  "authors": [
    {
      "name": "Dibakar Datta"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cx80a492x61b8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.34628",
  "fulltext_url": null,
  "id": "cx80a492x61b8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.34628",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7224008105",
  "pdf_url": "https://arxiv.org/pdf/2610.34628",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cx80a492x61b8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2610.34628",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.34628"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "From Autonomous Knowledge Generation to Generalizable Property Prediction: A Precision-First Pipeline for Electro-Optic Materials Discovery",
  "title_text": "From Autonomous Knowledge Generation to Generalizable Property Prediction: A Precision-First Pipeline for Electro-Optic Materials Discovery",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/cx80a492x61b8",
  "venue": "arXiv",
  "versions": []
}