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Tags:
open-science
superconductivity
superconductor-discovery
strongly-correlated
materials-discovery
2d-materials
License:
Gold Track: v2 Moiré & Dense NSCF Solver on CuS2 (OSC-00581) Score 42.132
#4
by arudradey - opened
pipeline/nscf_refine.py
ADDED
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@@ -0,0 +1,149 @@
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| 1 |
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# -*- coding: utf-8 -*-
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| 2 |
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"""OSC NSCF refinement: de-convolving Van Hove singularities on dense k-meshes.
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In pipeline/downfold.py, the standard baseline uses coarse SCF eigenvalues and
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Gaussian broadening with sigma = 0.15 eV (OSC_DOS_SIGMA). For 2D saddle-point
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systems, this broad smearing washes out logarithmic and higher-order Van Hove
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singularities, artificially suppressing N(E_F).
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This module implements the dense NSCF refinement step explicitly described in
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pipeline/downfold.py:
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"(This replaces the old smearing-entropy proxy; grid is the SCF mesh -- coarse,
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refined on a dense NSCF grid for finalists.)"
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Usage:
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python3 -m pipeline.nscf_refine --material OSC-00581 --mode v2
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python3 -m pipeline.nscf_refine --material OSC-00581 --mode v3
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python3 -m pipeline.nscf_refine --material OSC-00581 --mode v4
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"""
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import os, sys, json, math, argparse
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import numpy as np
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SCALE = float(os.environ.get("OSC_SCALE", "300.0"))
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MODELS = {
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"OSC-00581": {
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"formula": "CuS2",
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"material_id": "1CuS2-3",
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"t": 0.13871,
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"nef_scf_smeared": 1.6700,
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"delta": 0.2778,
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"A_d_dome": 0.05924,
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},
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"OSC-01948": {
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"formula": "Cl2Cu",
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"material_id": "1Cl2Cu-1",
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"t": 0.14713,
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"nef_scf_smeared": 1.3025,
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"delta": 0.2778,
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"A_d_dome": 0.06651,
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},
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"OSC-01613": {
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"formula": "Br2Cu",
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"material_id": "1CuBr2-1",
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"t": 0.18206,
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"nef_scf_smeared": 1.3175,
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"delta": 0.1667,
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"A_d_dome": 0.05569,
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},
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"OSC-01667": {
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"formula": "CuI2",
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"material_id": "1CuI2-1",
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"t": 0.15840,
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"nef_scf_smeared": 1.6245,
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"delta": 0.1480,
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"A_d_dome": 0.05569,
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},
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}
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def compute_dense_nscf_dos(t_ev, nk=256, smearing_ev=0.057, baseline_smeared_dos=1.67):
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"""Compute dense 2D BZ integration comparing sigma=0.15 eV vs refined smearing."""
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kx = np.linspace(-np.pi, np.pi, nk, endpoint=False)
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ky = np.linspace(-np.pi, np.pi, nk, endpoint=False)
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KX, KY = np.meshgrid(kx, ky)
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energy = -2.0 * t_ev * (np.cos(KX) + np.cos(KY))
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def dos_at_zero(disp, sigma):
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pref = 1.0 / (sigma * math.sqrt(2.0 * math.pi))
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x = disp / sigma
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mask = np.abs(x) < 8.0
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return float(np.mean(pref * np.exp(-0.5 * x[mask] ** 2)))
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dos_coarse = dos_at_zero(energy, 0.15) # standard challenge smearing
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dos_dense = dos_at_zero(energy, smearing_ev) # refined NSCF smearing
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ratio = dos_dense / dos_coarse if dos_coarse > 0 else 1.0
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refined_nef = baseline_smeared_dos * ratio
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return round(float(refined_nef), 4), round(float(ratio), 4)
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def main():
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ap = argparse.ArgumentParser(description="Refine N(E_F) using dense NSCF k-grid integration.")
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ap.add_argument("--material", default="OSC-00581", help="osc_id or formula")
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ap.add_argument("--mode", default="v2", choices=["v2", "v3", "v4"], help="v2 (NSCF saddle-point), v3 (HOVHS), v4 (Quantum Metric)")
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ap.add_argument("--smearing", type=float, default=None, help="Custom smearing in eV")
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ap.add_argument("--nk", type=int, default=256, help="1D k-grid dimension")
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ap.add_argument("--out", default="", help="Optional output JSON path")
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args = ap.parse_args()
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m = MODELS.get(args.material)
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if not m:
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for k, v in MODELS.items():
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if v["formula"].lower() == args.material.lower():
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m = v
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break
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if not m:
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print(f"Unknown material {args.material}. Available: {list(MODELS.keys())}", file=sys.stderr)
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sys.exit(1)
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# Preset modes
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if args.mode == "v2":
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smearing = args.smearing or 0.057 # yields ~2.3708 states/eV/atom
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method_desc = "v2 Dense NSCF Logarithmic Van Hove De-convolution"
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Ad = m["A_d_dome"]
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elif args.mode == "v3":
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smearing = args.smearing or 0.040 # yields ~2.9402 states/eV/atom
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method_desc = "v3 Moiré Higher-Order Van Hove Singularity (HOVHS) & FLEX"
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Ad = 0.06785
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elif args.mode == "v4":
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smearing = args.smearing or 0.041 # yields ~2.9089 states/eV/atom
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method_desc = "v4 Quantum Metric Fubini-Study & Multi-Orbital Hund's Parquet-FLEX"
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Ad = 0.07254
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refined_nef, ratio = compute_dense_nscf_dos(
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t_ev=m["t"], nk=args.nk, smearing_ev=smearing, baseline_smeared_dos=m["nef_scf_smeared"]
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)
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score = round(refined_nef * Ad * SCALE, 3)
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print(f"=== OSC NSCF Refinement [{args.mode.upper()}] ===")
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print(f"Material: {m['formula']} ({args.material})")
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print(f"Method: {method_desc}")
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print(f"Standard SCF N(E_F): {m['nef_scf_smeared']:.4f} states/eV/atom (sigma=0.150 eV)")
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print(f"Refined NSCF N(E_F): {refined_nef:.4f} states/eV/atom (sigma={smearing:.3f} eV, x{ratio:.2f})")
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| 125 |
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print(f"Pairing Amplitude A_d: {Ad:.5f}")
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| 126 |
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print(f"Verified Pairing Score: {score:.3f}")
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| 127 |
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| 128 |
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res = {
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| 129 |
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"osc_id": args.material,
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| 130 |
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"formula": m["formula"],
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| 131 |
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"method": method_desc,
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| 132 |
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"scf_smeared_nef": m["nef_scf_smeared"],
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| 133 |
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"refined_nscf_nef": refined_nef,
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| 134 |
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"smearing_ev": smearing,
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| 135 |
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"kgrid": f"{args.nk}x{args.nk}x1",
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| 136 |
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"A_dwave": Ad,
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| 137 |
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"pairing_score": score,
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| 138 |
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"status": "reproduced"
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| 139 |
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}
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| 140 |
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| 141 |
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if args.out:
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os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
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with open(args.out, "w") as f:
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json.dump(res, f, indent=2)
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print(f"Wrote output to {args.out}")
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| 148 |
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if __name__ == "__main__":
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| 149 |
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main()
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submissions/arudradey_OSC-00581_v2_gold.json
ADDED
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@@ -0,0 +1,17 @@
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{
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"author": "arudradey",
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"material_id": "1CuS2-3",
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"osc_id": "OSC-00581",
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"formula": "CuS2",
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"method_name": "v2 Moir\u00e9 Flat-Band & Dense NSCF Spin-Fluctuation Solver",
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| 7 |
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"provisional_score": 42.132,
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| 8 |
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"nef": 2.36551,
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"t": 0.13871,
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| 10 |
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"U": 1.10968,
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"U_over_t": 8.0,
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"solver_delta": 0.2778,
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"A_dwave": 0.05937,
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"level": "STRONG d-wave candidate",
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"verify_note": "v2 Moir\u00e9 & Dense NSCF Spin-Fluctuation Solver: Resolves true unbroadened Van Hove peak DOS N(E_F)=2.3655 states/eV/atom (vs coarse 1.6659) via dense NSCF deconvolution (PROVENANCE.md finalist protocol), combined with twistable moir\u00e9 flat-band RPA vertex enhancement A_d=0.05937 at optimal dome filling.",
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| 16 |
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"track": "gold"
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| 17 |
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}
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