{
  "model": "Hchain",
  "lattice": "chain",
  "n_sites": 8,
  "boundary": "O",
  "params": {
    "N_up": 4,
    "N_dn": 4,
    "R": 1.8,
    "basis": "STO-6G"
  },
  "instance_id": "Hchain/chain_8_O_R1.8_sto6g",
  "rows": [
    {
      "energy": -4.34406,
      "sigma": null,
      "energy_variance": null,
      "dof": 8,
      "einf": null,
      "v_score": null,
      "method": "VMC",
      "ansatz": "RBM",
      "method_detail": "canonical orbitals, selected-configuration (SC) iteration",
      "method_as_published": "CMO (NQS optimized using the SC iteration scheme)",
      "family": "RBM",
      "bound_type": "variational",
      "bound_type_reason": "NQS (RBM) optimised with SC iteration; Table S2 caption does not say variational energy (eq 13) or optimisation-time energy (eq 12)",
      "reference": "Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang et al., A Nonstochastic Optimization Algorithm for Neural-Network Quantum States, Journal of Chemical Theory and Computation 19, 8156-8165 (2023), arXiv:2305.12776, doi:10.1021/acs.jctc.3c00831",
      "peer_reviewed": true,
      "source": "track3-2026-10-20",
      "provenance": "primary",
      "verified": {
        "checked_on": "2026-10-20",
        "method": "source text of arXiv:2305.12776 read locally (pypdf layout text; the generator finds every value again on its line), no LLM transcription of numbers; single reading (Sonnet reader)",
        "reported_as": "−4.34406 Ha per total",
        "note": "Table S2; sources/2305.12776.txt line 1152; version read: arXiv text; journal copy not reached (publisher returned HTTP 403 or a login page, 2026-10-21); read again when reachable (RULES.md §8) | line 1143: 'ground-state energies'; no unit printed; H10 FCI 1.8 bohr -5.42439 = -0.5424/atom, matches Motta 2017 total Ha incl. nuclear repulsion | Unit Ha and nuclear repulsion inferred from agreement with the known H10 STO-6G value, not stated. CMO/LMO = NQS in canonical/localized MO basis. | reader confidence medium",
        "secondary_of": null,
        "second_read": {
          "checked_on": "2026-10-24",
          "scope": "value",
          "method": "arXiv:2305.12776v2 PDF (29 Jul 2023), pypdf layout and plain modes (both parse the same 8 cells per row); v1 (22 May 2023) has no Table S2 and no H4-H8 values; JCTC 19, 8156 (2023) journal copy and SI not reached (pubs.acs.org returned HTTP 403 Cloudflare challenge for article, PDF and suppl pages, 2026-10-24)",
          "location": "Table S2 (SI p. S4, PDF page 30), row H8, column CMO under 1.8 Bohr; my extraction 2305.12776v2-layout.txt line 1182",
          "reported_as": "−4.34406",
          "conversion": "printed value is already the total in Ha: -4.34406 Ha total = -0.5430075000 Ha per atom (divided by N = 8); stored eps per atom matches",
          "note": "Instance (open chain, N = 8, R = 1.8 bohr, STO-6G, neutral, Sz = 0) and column agree. Total in Ha with nuclear repulsion: the same table's FCI cell agrees with QMBL's pyscf FCI -4.3450794 Ha to the printed 5 decimals, so unit and constant are fixed although the paper prints no unit. Printed value - QMBL FCI = 1.02e-3 Ha. Deterministic selected-configuration (SC) RBM NQS, hidden-unit density alpha = 4, lambda = 1e-5, selection cutoff 1e-5 (SI section 4); no sigma exists (non-stochastic); sampled false is right. Not below FCI. No other row on this instance from another batch (sweep-rows has only this paper's 4 rows here; data/Hchain does not exist): no duplicate.",
          "secondary_of": null,
          "source_entry": "h-p-2305-12776-c1#20 (qmbl-verify 2026-10-23/24, verified)"
        }
      },
      "sampled": false,
      "corrections": [
        {
          "field": "sampled",
          "to": false,
          "reported_as": "−4.34406 Ha per total",
          "location": "Table S2; sources/2305.12776.txt line 1152; version read: arXiv text; journal copy not reached (publisher returned HTTP 403 or a login page, 2026-10-21); read again when reachable (RULES.md §8)",
          "checked_on": "2026-10-20",
          "reason": "Deterministic, not a Monte Carlo estimate, as the track-3 reader recorded it (pass row `sampled`); the method string alone reads the other way (units.mjs, isSampled).",
          "source_entry": "passes/track3/sweep-rows-2026-10-20-hydrogen.json"
        }
      ]
    },
    {
      "energy": -4.34507,
      "sigma": null,
      "energy_variance": null,
      "dof": 8,
      "einf": null,
      "v_score": null,
      "method": "VMC",
      "ansatz": "RBM",
      "method_detail": "localized orbitals, selected-configuration (SC) iteration",
      "method_as_published": "LMO (NQS optimized using the SC iteration scheme)",
      "family": "RBM",
      "bound_type": "variational",
      "bound_type_reason": "NQS (RBM) optimised with SC iteration; Table S2 caption does not say variational energy (eq 13) or optimisation-time energy (eq 12)",
      "reference": "Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang et al., A Nonstochastic Optimization Algorithm for Neural-Network Quantum States, Journal of Chemical Theory and Computation 19, 8156-8165 (2023), arXiv:2305.12776, doi:10.1021/acs.jctc.3c00831",
      "peer_reviewed": true,
      "source": "track3-2026-10-20",
      "provenance": "primary",
      "verified": {
        "checked_on": "2026-10-20",
        "method": "source text of arXiv:2305.12776 read locally (pypdf layout text; the generator finds every value again on its line), no LLM transcription of numbers; single reading (Sonnet reader)",
        "reported_as": "−4.34507 Ha per total",
        "note": "Table S2; sources/2305.12776.txt line 1152; version read: arXiv text; journal copy not reached (publisher returned HTTP 403 or a login page, 2026-10-21); read again when reachable (RULES.md §8) | line 1143: 'ground-state energies'; no unit printed; H10 FCI 1.8 bohr -5.42439 = -0.5424/atom, matches Motta 2017 total Ha incl. nuclear repulsion | Unit Ha and nuclear repulsion inferred from agreement with the known H10 STO-6G value, not stated. CMO/LMO = NQS in canonical/localized MO basis. | Variational (ruled 2026-10-24). The paper does not state whether the printed LMO energy is the optimisation-time estimator of eq. 12 or the variational energy of the truncated state of eq. 13 at the selection cutoff 1e-3 used for these runs; it calls the two equal only for cutoffs up to 1e-5. | reader confidence medium",
        "secondary_of": null,
        "second_read": {
          "checked_on": "2026-10-24",
          "scope": "value",
          "method": "arXiv:2305.12776v2 PDF (29 Jul 2023), pypdf layout and plain modes (both parse the same 8 cells per row); v1 (22 May 2023) has no Table S2 and no H4-H8 values; JCTC 19, 8156 (2023) journal copy and SI not reached (pubs.acs.org returned HTTP 403 Cloudflare challenge for article, PDF and suppl pages, 2026-10-24)",
          "location": "Table S2 (SI p. S4, PDF page 30), row H8, column LMO under 1.8 Bohr; my extraction 2305.12776v2-layout.txt line 1182",
          "reported_as": "−4.34507",
          "conversion": "printed value is already the total in Ha: -4.34507 Ha total = -0.5431337500 Ha per atom (divided by N = 8); stored eps per atom matches",
          "note": "Instance (open chain, N = 8, R = 1.8 bohr, STO-6G, neutral, Sz = 0) and column agree. Total in Ha with nuclear repulsion: the same table's FCI cell agrees with QMBL's pyscf FCI -4.3450794 Ha to the printed 5 decimals, so unit and constant are fixed although the paper prints no unit. Printed value - QMBL FCI = 9.40e-6 Ha. Deterministic selected-configuration (SC) RBM NQS, hidden-unit density alpha = 4, lambda = 1e-5, selection cutoff 1e-3 (SI section 4); no sigma exists (non-stochastic); sampled false is right. Not below FCI. No other row on this instance from another batch (sweep-rows has only this paper's 4 rows here; data/Hchain does not exist): no duplicate.",
          "secondary_of": null,
          "source_entry": "h-p-2305-12776-c1#21 (qmbl-verify 2026-10-23/24, verified)"
        }
      },
      "sampled": false,
      "corrections": [
        {
          "field": "note",
          "from": "Unit Ha and nuclear repulsion inferred from agreement with the known H10 STO-6G value, not stated. CMO/LMO = NQS in canonical/localized MO basis.",
          "to": "Unit Ha and nuclear repulsion inferred from agreement with the known H10 STO-6G value, not stated. CMO/LMO = NQS in canonical/localized MO basis. | Variational (ruled 2026-10-24). The paper does not state whether the printed LMO energy is the optimisation-time estimator of eq. 12 or the variational energy of the truncated state of eq. 13 at the selection cutoff 1e-3 used for these runs; it calls the two equal only for cutoffs up to 1e-5.",
          "checked_on": "2026-10-24",
          "reason": "note added by the verify pass; the earlier note is kept in front",
          "source_entry": "h-p-2305-12776-c1#22 (qmbl-verify 2026-10-23/24, ambiguous); ruling 9, 2026-10-24"
        },
        {
          "field": "sampled",
          "to": false,
          "reported_as": "−4.34507 Ha per total",
          "location": "Table S2; sources/2305.12776.txt line 1152; version read: arXiv text; journal copy not reached (publisher returned HTTP 403 or a login page, 2026-10-21); read again when reachable (RULES.md §8)",
          "checked_on": "2026-10-20",
          "reason": "Deterministic, not a Monte Carlo estimate, as the track-3 reader recorded it (pass row `sampled`); the method string alone reads the other way (units.mjs, isSampled).",
          "source_entry": "passes/track3/sweep-rows-2026-10-20-hydrogen.json"
        }
      ]
    },
    {
      "energy": -4.345,
      "sigma": null,
      "energy_variance": null,
      "dof": 8,
      "einf": null,
      "v_score": null,
      "method": "CCSD(T)",
      "ansatz": null,
      "method_detail": "",
      "method_as_published": "CCSD(T)",
      "family": "FCIQMC / CC",
      "bound_type": "approximate",
      "bound_type_reason": "CCSD(T) unsettled (RULES 13.4)",
      "reference": "Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang et al., A Nonstochastic Optimization Algorithm for Neural-Network Quantum States, Journal of Chemical Theory and Computation 19, 8156-8165 (2023), arXiv:2305.12776, doi:10.1021/acs.jctc.3c00831",
      "peer_reviewed": true,
      "source": "track3-2026-10-20",
      "provenance": "primary",
      "verified": {
        "checked_on": "2026-10-20",
        "method": "source text of arXiv:2305.12776 read locally (pypdf layout text; the generator finds every value again on its line), no LLM transcription of numbers; single reading (Sonnet reader)",
        "reported_as": "−4.34500 Ha per total",
        "note": "Table S2; sources/2305.12776.txt line 1152; version read: arXiv text; journal copy not reached (publisher returned HTTP 403 or a login page, 2026-10-21); read again when reachable (RULES.md §8) | line 1143: 'ground-state energies'; no unit printed; H10 FCI 1.8 bohr -5.42439 = -0.5424/atom, matches Motta 2017 total Ha incl. nuclear repulsion | Unit Ha and nuclear repulsion inferred from agreement with the known H10 STO-6G value, not stated. CMO/LMO = NQS in canonical/localized MO basis. | reader confidence medium",
        "secondary_of": null,
        "second_read": {
          "checked_on": "2026-10-24",
          "scope": "value",
          "method": "arXiv:2305.12776v2 PDF (29 Jul 2023), pypdf layout and plain modes (both parse the same 8 cells per row); v1 (22 May 2023) has no Table S2 and no H4-H8 values; JCTC 19, 8156 (2023) journal copy and SI not reached (pubs.acs.org returned HTTP 403 Cloudflare challenge for article, PDF and suppl pages, 2026-10-24)",
          "location": "Table S2 (SI p. S4, PDF page 30), row H8, column CCSD(T) under 1.8 Bohr; my extraction 2305.12776v2-layout.txt line 1182",
          "reported_as": "−4.34500",
          "conversion": "printed value is already the total in Ha: -4.345 Ha total = -0.5431250000 Ha per atom (divided by N = 8); stored eps per atom matches",
          "note": "Instance (open chain, N = 8, R = 1.8 bohr, STO-6G, neutral, Sz = 0) and column agree. Total in Ha with nuclear repulsion: the same table's FCI cell agrees with QMBL's pyscf FCI -4.3450794 Ha to the printed 5 decimals, so unit and constant are fixed although the paper prints no unit. Printed value - QMBL FCI = 7.94e-5 Ha. Not below FCI within printed digits; bound_type approximate per RULES 13.4 (track 3, 2026-10-21). No other row on this instance from another batch (sweep-rows has only this paper's 4 rows here; data/Hchain does not exist): no duplicate.",
          "secondary_of": null,
          "source_entry": "h-p-2305-12776-c1#23 (qmbl-verify 2026-10-23/24, verified)"
        }
      },
      "sampled": false,
      "corrections": [
        {
          "field": "sampled",
          "to": false,
          "reported_as": "−4.34500 Ha per total",
          "location": "Table S2; sources/2305.12776.txt line 1152; version read: arXiv text; journal copy not reached (publisher returned HTTP 403 or a login page, 2026-10-21); read again when reachable (RULES.md §8)",
          "checked_on": "2026-10-20",
          "reason": "Deterministic, not a Monte Carlo estimate, as the track-3 reader recorded it (pass row `sampled`); the method string alone reads the other way (units.mjs, isSampled).",
          "source_entry": "passes/track3/sweep-rows-2026-10-20-hydrogen.json"
        }
      ]
    },
    {
      "energy": -4.34508,
      "sigma": null,
      "energy_variance": null,
      "dof": 8,
      "einf": null,
      "v_score": null,
      "method": "FCI",
      "ansatz": null,
      "method_detail": "",
      "method_as_published": "FCI",
      "family": "other",
      "bound_type": "exact",
      "bound_type_reason": "FCI in STO-6G",
      "reference": "Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang et al., A Nonstochastic Optimization Algorithm for Neural-Network Quantum States, Journal of Chemical Theory and Computation 19, 8156-8165 (2023), arXiv:2305.12776, doi:10.1021/acs.jctc.3c00831",
      "peer_reviewed": true,
      "source": "track3-2026-10-20",
      "provenance": "primary",
      "verified": {
        "checked_on": "2026-10-20",
        "method": "source text of arXiv:2305.12776 read locally (pypdf layout text; the generator finds every value again on its line), no LLM transcription of numbers; single reading (Sonnet reader)",
        "reported_as": "−4.34508 Ha per total",
        "note": "Table S2; sources/2305.12776.txt line 1152; version read: arXiv text; journal copy not reached (publisher returned HTTP 403 or a login page, 2026-10-21); read again when reachable (RULES.md §8) | line 1143: 'ground-state energies'; no unit printed; H10 FCI 1.8 bohr -5.42439 = -0.5424/atom, matches Motta 2017 total Ha incl. nuclear repulsion | Unit Ha and nuclear repulsion inferred from agreement with the known H10 STO-6G value, not stated. CMO/LMO = NQS in canonical/localized MO basis. | reader confidence medium",
        "secondary_of": null,
        "second_read": {
          "checked_on": "2026-10-24",
          "scope": "value",
          "method": "arXiv:2305.12776v2 PDF (29 Jul 2023), pypdf layout and plain modes (both parse the same 8 cells per row); v1 (22 May 2023) has no Table S2 and no H4-H8 values; JCTC 19, 8156 (2023) journal copy and SI not reached (pubs.acs.org returned HTTP 403 Cloudflare challenge for article, PDF and suppl pages, 2026-10-24)",
          "location": "Table S2 (SI p. S4, PDF page 30), row H8, column FCI under 1.8 Bohr; my extraction 2305.12776v2-layout.txt line 1182",
          "reported_as": "−4.34508",
          "conversion": "printed value is already the total in Ha: -4.34508 Ha total = -0.5431350000 Ha per atom (divided by N = 8); stored eps per atom matches",
          "note": "Instance (open chain, N = 8, R = 1.8 bohr, STO-6G, neutral, Sz = 0) and column agree. Total in Ha with nuclear repulsion: the same table's FCI cell agrees with QMBL's pyscf FCI -4.3450794 Ha to the printed 5 decimals, so unit and constant are fixed although the paper prints no unit. Printed value - QMBL FCI = -5.97e-7 Ha. Exact; agrees with QMBL's recompute within half a unit of the 5th decimal (|diff| 5.97e-7 < 5e-6). Only exact row on the instance. No other row on this instance from another batch (sweep-rows has only this paper's 4 rows here; data/Hchain does not exist): no duplicate.",
          "secondary_of": null,
          "source_entry": "h-p-2305-12776-c1#24 (qmbl-verify 2026-10-23/24, verified)"
        }
      }
    }
  ],
  "url": "https://qmbl.org/i/Hchain/chain_8_O_R1.8_sto6g/",
  "per_site_divisor": 8,
  "per_site_label": "E/N (Ha)",
  "best": {
    "exact": {
      "energy": -4.34508,
      "sigma": null,
      "method": "FCI",
      "ansatz": null,
      "method_detail": "",
      "reference": "Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang et al., A Nonstochastic Optimization Algorithm for Neural-Network Quantum States, Journal of Chemical Theory and Computation 19, 8156-8165 (2023), arXiv:2305.12776, doi:10.1021/acs.jctc.3c00831",
      "bound_type": "exact",
      "energy_per_site": -0.543135
    },
    "variational": {
      "energy": -4.34507,
      "sigma": null,
      "method": "VMC",
      "ansatz": "RBM",
      "method_detail": "localized orbitals, selected-configuration (SC) iteration",
      "reference": "Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang et al., A Nonstochastic Optimization Algorithm for Neural-Network Quantum States, Journal of Chemical Theory and Computation 19, 8156-8165 (2023), arXiv:2305.12776, doi:10.1021/acs.jctc.3c00831",
      "bound_type": "variational",
      "energy_per_site": -0.54313375
    },
    "projected": null,
    "extrapolated": null,
    "approximate": {
      "energy": -4.345,
      "sigma": null,
      "method": "CCSD(T)",
      "ansatz": null,
      "method_detail": "",
      "reference": "Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang et al., A Nonstochastic Optimization Algorithm for Neural-Network Quantum States, Journal of Chemical Theory and Computation 19, 8156-8165 (2023), arXiv:2305.12776, doi:10.1021/acs.jctc.3c00831",
      "bound_type": "approximate",
      "energy_per_site": -0.543125
    },
    "below_exact": []
  },
  "no_best_reason": null,
  "record": {
    "energy": -4.34508,
    "sigma": null,
    "method": "FCI",
    "ansatz": null,
    "method_detail": "",
    "reference": "Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang et al., A Nonstochastic Optimization Algorithm for Neural-Network Quantum States, Journal of Chemical Theory and Computation 19, 8156-8165 (2023), arXiv:2305.12776, doi:10.1021/acs.jctc.3c00831",
    "bound_type": "exact",
    "energy_per_site": -0.543135
  },
  "no_record_reason": null
}