{
  "abstract": "Superconductors with high critical temperatures that emerges beyond the phonon-mediated regime are usually considered unconventional in nature, yet unlike conventional superconductors, no broadly applicable predictive theory currently guides their discovery. Here, we use interpretable machine learning to uncover a common materials-space signature of high-$T_{\\mathrm{c}}$ unconventional superconductors and develop a data-driven strategy for materials discovery. We construct a unified feature representation for each material by integrating compositional statistics, structural information, and latent representations from trained property-prediction models, followed by structure-aware filtering of an experimentally established superconducting dataset. Without using transition-temperature information, unsupervised analysis shows that cuprate and iron-based superconductors occupy a common region of materials space, characterized primarily by large electronegativity deviation and intermediate mean valence-electron number. A supervised $T_{\\text{c}}$ model independently identifies the same descriptors as dominant features, providing complementary evidence for their relevance. Using this empirical materials-space prior together with the $T_{\\text{c}}$ model, we prioritize candidate materials, recover recently discovered nickelate superconductors, and identify chemically distinct candidates for future investigation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.30588",
  "author_total": 4,
  "authors": [
    {
      "name": "Haosheng Xu"
    },
    {
      "name": "Dongheng Qian"
    },
    {
      "name": "Yijun Yu"
    },
    {
      "name": "Jing Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/401mpjrq0tb4y.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.30588",
  "fulltext_url": null,
  "id": "401mpjrq0tb4y",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.30588",
  "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.supr-con"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7204944330",
  "pdf_url": "https://arxiv.org/pdf/2608.30588",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/401mpjrq0tb4y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:41Z",
      "native_id": "2608.30588",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.30588"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.supr-con",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Machine learning reveals common features of unconventional superconductors with high transition temperatures",
  "title_text": "Machine learning reveals common features of unconventional superconductors with high transition temperatures",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:41Z",
  "url": "https://research.draconitic.com/paper/401mpjrq0tb4y",
  "venue": "arXiv",
  "versions": []
}