{
  "model": "Heisenberg",
  "lattice": "triangular",
  "n_sites": 108,
  "boundary": "P",
  "params": {},
  "instance_id": "Heisenberg/triangular_108_P",
  "rows": [
    {
      "energy": -238.9608,
      "sigma": 0.01296,
      "energy_variance": null,
      "dof": 108,
      "einf": 0,
      "v_score": null,
      "method": "GCNN (deep group-equivariant CNN)",
      "bound_type": "variational",
      "bound_type_reason": "assigned during source verification (RULES.md 4)",
      "reference": "Roth, Szabo & MacDonald, High-accuracy variational Monte Carlo for frustrated magnets with deep neural networks, Phys. Rev. B 108, 054410 (2023), arXiv:2211.07749",
      "peer_reviewed": true,
      "source": "sweep-pdf-2026-09-14",
      "provenance": "primary",
      "verified": {
        "checked_on": "2026-09-14",
        "method": "arXiv PDF extracted locally with pypdf in layout mode, no LLM transcription",
        "reported_as": "-0.55315 (+/- 0.00003) per site in S.S units",
        "note": "Read from Table III of arXiv:2211.07749 (J1-J2 triangular lattice, periodic, energies in units of J1 per spin, i.e. the S.S per-site convention), extracted from the PDF with pypdf in layout mode so the column positions are unambiguous. Row J2/J1 = 0, N = 108.",
        "secondary_of": null
      }
    },
    {
      "energy": -238.4208,
      "sigma": 0.1728,
      "energy_variance": null,
      "dof": 108,
      "einf": 0,
      "v_score": null,
      "method": "Graph neural network",
      "bound_type": "variational",
      "bound_type_reason": "assigned during source verification (RULES.md 4)",
      "reference": "Kochkov, Pfaff, Sanchez-Gonzalez, Battaglia & Clark, Learning ground states of quantum Hamiltonians with graph networks, arXiv:2110.06390 (2021)",
      "peer_reviewed": null,
      "source": "sweep-pdf-2026-09-14",
      "provenance": "secondary",
      "verified": {
        "checked_on": "2026-09-14",
        "method": "arXiv PDF extracted locally with pypdf in layout mode, no LLM transcription",
        "reported_as": "-0.5519 (+/- 0.0004) per site in S.S units",
        "note": "Read from Table III of arXiv:2211.07749 (J1-J2 triangular lattice, periodic, energies in units of J1 per spin, i.e. the S.S per-site convention), extracted from the PDF with pypdf in layout mode so the column positions are unambiguous. Column \"Graph NN [64]\" at J2/J1 = 0, N = 108 = arXiv:2110.06390.",
        "secondary_of": "Roth, Szabo & MacDonald, High-accuracy variational Monte Carlo for frustrated magnets with deep neural networks, Phys. Rev. B 108, 054410 (2023), arXiv:2211.07749"
      }
    }
  ],
  "url": "https://qmbl.org/i/Heisenberg/triangular_108_P/",
  "per_site_divisor": 432,
  "per_site_label": "E/N (S.S)",
  "record": {
    "energy": -238.9608,
    "sigma": 0.01296,
    "method": "GCNN (deep group-equivariant CNN)",
    "reference": "Roth, Szabo & MacDonald, High-accuracy variational Monte Carlo for frustrated magnets with deep neural networks, Phys. Rev. B 108, 054410 (2023), arXiv:2211.07749",
    "energy_per_site": -0.55315
  },
  "no_record_reason": null
}