{
  "abstract": "Advances in experimental instrumentation and automation generate increasingly rich datasets, but turning experimental observations into microscopic understanding remains a bottleneck in scientific discovery. To accelerate this process, we introduce AI Theorist, a system of artificial intelligence (AI) agents for autonomous discovery of physical models through hypothesis generation, first-principles calculations and evidence-driven refinement. We apply the framework to $α$-RuCl$_3$, a leading candidate material for realizing a Kitaev quantum spin liquid, to investigate its electronic structure through optical spectra. AI Theorist develops a new interpretation of the optical and photocurrent observations, identifying distinct excitonic states with contrasting optical selection rules and real-space distributions. To our knowledge, this is the first demonstration of an AI system autonomously developing a physical model to explain previously unpublished experimental observations in a quantum material, utilizing first-principles electronic-structure and many-body calculations. Our results establish a route to autonomous theoretical discovery in materials science, in which AI agents use first-principles calculations to turn experimental observations into physical models and testable predictions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.02417",
  "author_total": 8,
  "authors": [
    {
      "name": "Hongjian Zhou"
    },
    {
      "name": "Xianfan Nie"
    },
    {
      "name": "Sean Wu"
    },
    {
      "name": "Tarun Patel"
    },
    {
      "name": "Jinge Wu"
    },
    {
      "name": "Andrew Liu"
    },
    {
      "name": "Adam Wei Tsen"
    },
    {
      "name": "David A. Clifton"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8aaymk637nnkx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.02417",
  "fulltext_url": null,
  "id": "8aaymk637nnkx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.02417",
  "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"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220367160",
  "pdf_url": "https://arxiv.org/pdf/2610.02417",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8aaymk637nnkx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:45Z",
      "native_id": "2610.02417",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.02417"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "The AI Theorist reveals excitonic structure in $α$-RuCl$_3$",
  "title_text": "The AI Theorist reveals excitonic structure in α-RuCl_3",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:45Z",
  "url": "https://research.draconitic.com/paper/8aaymk637nnkx",
  "venue": "arXiv",
  "versions": []
}