{
  "model": "J1J2",
  "lattice": "triangular",
  "n_sites": 144,
  "boundary": "P",
  "params": {
    "J2": 0.125
  },
  "instance_id": "J1J2/triangular_144_P_0.125",
  "rows": [
    {
      "energy": -295.01568,
      "sigma": 0.05184,
      "energy_variance": null,
      "dof": 144,
      "einf": 0,
      "v_score": null,
      "method": "GCNN + Lanczos step",
      "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": "-0.51218 (+/- 0.00009) 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 \"GCNN+LS\" at J2/J1 = 1/8, N = 144.",
        "secondary_of": null
      }
    },
    {
      "energy": -294.34176,
      "sigma": 0.03456,
      "energy_variance": null,
      "dof": 144,
      "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": "-0.51101 (+/- 0.00006) 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 = 1/8, N = 144.",
        "secondary_of": null
      }
    },
    {
      "energy": -294.081408,
      "sigma": 0.00288,
      "energy_variance": null,
      "dof": 144,
      "einf": 0,
      "v_score": null,
      "method": "Gutzwiller-projected fermionic state + Lanczos step",
      "bound_type": "variational",
      "bound_type_reason": "assigned during source verification (RULES.md 4)",
      "reference": "Iqbal, Hu, Thomale, Poilblanc & Becca, Spin liquid nature in the Heisenberg J1-J2 triangular antiferromagnet, Phys. Rev. B 93, 144411 (2016)",
      "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": "-0.510558 (+/- 0.000005) 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 \"Gutzwiller+LS [10]\" = Phys. Rev. B 93, 144411 (2016).",
        "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"
      }
    },
    {
      "energy": -295.344,
      "sigma": null,
      "energy_variance": null,
      "dof": 144,
      "einf": 0,
      "v_score": null,
      "method": "Thermodynamic-limit estimate interpolated to this size (1/L^3)",
      "bound_type": "extrapolated",
      "bound_type_reason": "assigned during source verification (RULES.md 4)",
      "reference": "Iqbal, Hu, Thomale, Poilblanc & Becca, Spin liquid nature in the Heisenberg J1-J2 triangular antiferromagnet, Phys. Rev. B 93, 144411 (2016)",
      "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": "-0.51275 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 \"Exact/Interpolated [10,63]\". Per the caption this is NOT an exact energy above 36 sites: it is built from the thermodynamic-limit estimate of Ref. [10] and the exact 36-site energy, assuming finite-size effects scale as 1/L^3. Carried as `extrapolated` (RULES.md 4).",
        "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/J1J2/triangular_144_P_0.125/",
  "per_site_divisor": 576,
  "per_site_label": "E/N (S.S)",
  "record": {
    "energy": -295.01568,
    "sigma": 0.05184,
    "method": "GCNN + Lanczos step",
    "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": -0.51218
  },
  "no_record_reason": null
}