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df32294 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 | """Phase 7: Scientific Audit — property distributions, domain plausibility."""
import json, time
from pathlib import Path
from collections import Counter
import numpy as np
OUT = Path("scripts/audit_reports")
DATASET = "dataset/entries_final_v3.json"
AUDIT_DIR = Path.cwd() if Path.cwd().name == "Scandium-Dataset" else Path("/home/shamique/Scandium Labs SSB/Scandium-Dataset")
severity_counts = {"CRITICAL": 0, "HIGH": 0, "MEDIUM": 0, "LOW": 0, "PASS": 0}
findings = []
def finding(severity, phase, check, status, detail):
severity_counts[severity] += 1
findings.append({"severity": severity, "phase": phase, "check": check, "status": status, "detail": str(detail)[:200]})
s = "🔴" if severity == "CRITICAL" else "🟠" if severity == "HIGH" else "🟡" if severity == "MEDIUM" else "🔵" if severity == "LOW" else "✅"
print(f" {s} [{severity:8s}] {check}: {str(detail)[:120]}")
def main():
print("=" * 60)
print(" PHASE 7: SCIENTIFIC AUDIT")
print("=" * 60)
with open(AUDIT_DIR / DATASET) as f:
entries = json.load(f)
N = len(entries)
# Extract all labels
fe = np.array([e.get("formation_energy_per_atom", np.nan) for e in entries], dtype=float)
eah = np.array([e.get("energy_above_hull", np.nan) for e in entries], dtype=float)
bg = np.array([e.get("band_gap", np.nan) for e in entries], dtype=float)
vol = np.array([e.get("volume", np.nan) for e in entries], dtype=float)
dens = np.array([e.get("density", np.nan) for e in entries], dtype=float)
sg = [e.get("space_group") for e in entries]
nelem = np.array([len(e.get("elements", [])) for e in entries], dtype=float)
# FE distribution
fe_valid = fe[~np.isnan(fe)]
print(f"\n--- Formation Energy ---")
print(f" N={len(fe_valid):,} Range: [{np.min(fe_valid):.2f}, {np.max(fe_valid):.2f}] eV/atom")
print(f" Mean={np.mean(fe_valid):.2f} Median={np.median(fe_valid):.2f} Std={np.std(fe_valid):.2f}")
# Expected range for solid-state materials: [-6, 4] eV/atom
extreme_fe = fe_valid[(fe_valid < -6) | (fe_valid > 4)]
n_extreme = len(extreme_fe)
if n_extreme > 100:
finding("HIGH", "scientific", "extreme_formation_energy_per_atom",
f"{n_extreme:,} entries outside [-6, 4] eV", f"max={np.max(extreme_fe):.1f}, min={np.min(extreme_fe):.1f}")
elif n_extreme > 0:
finding("MEDIUM", "scientific", "extreme_formation_energy_per_atom",
f"{n_extreme} outliers", "")
else:
finding("PASS", "scientific", "formation_energy_plausible", "all in [-6, 4]", "")
# FE quantiles
for q in [1, 5, 25, 50, 75, 95, 99]:
print(f" P{q:2d}: {np.percentile(fe_valid, q):7.2f} eV/atom")
# EaH distribution
eah_valid = eah[~np.isnan(eah)]
print(f"\n--- Energy Above Hull ---")
print(f" N={len(eah_valid):,} Range: [{np.min(eah_valid):.2f}, {np.max(eah_valid):.2f}] eV/atom")
print(f" Mean={np.mean(eah_valid):.2f} Median={np.median(eah_valid):.2f} Std={np.std(eah_valid):.2f}")
extreme_eah = eah_valid[eah_valid > 1]
n_eah_extreme = len(extreme_eah)
if n_eah_extreme > 100:
finding("MEDIUM", "scientific", "high_energy_above_hull",
f"{n_eah_extreme:,} entries > 1 eV/atom", f"max={np.max(extreme_eah):.1f}")
elif n_eah_extreme > 0:
finding("MEDIUM", "scientific", "high_energy_above_hull",
f"{n_eah_extreme} entries > 1 eV", "")
else:
finding("PASS", "scientific", "high_energy_above_hull", "all ≤ 1 eV/atom", "")
# Band gap distribution
bg_valid = bg[~np.isnan(bg)]
print(f"\n--- Band Gap ---")
print(f" N={len(bg_valid):,} Range: [{np.min(bg_valid):.2f}, {np.max(bg_valid):.2f}] eV")
print(f" Mean={np.mean(bg_valid):.2f} Median={np.median(bg_valid):.2f} Std={np.std(bg_valid):.2f}")
# Metal distribution
n_metal = int(np.sum(bg_valid <= 0.1))
n_small = int(np.sum((bg_valid > 0.1) & (bg_valid <= 0.5)))
n_insulator = int(np.sum(bg_valid > 4))
print(f" Metals (≤0.1 eV): {n_metal:,} ({100*n_metal/len(bg_valid):.1f}%)")
print(f" Narrow-gap (0.1-0.5): {n_small:,} ({100*n_small/len(bg_valid):.1f}%)")
print(f" Wide-gap (>4 eV): {n_insulator:,} ({100*n_insulator/len(bg_valid):.1f}%)")
# Volume vs density sanity
vol_valid = vol[~np.isnan(vol)]
dens_valid = dens[~np.isnan(dens)]
print(f"\n--- Volume vs Density ---")
print(f" Volume range: [{np.min(vol_valid):.0f}, {np.max(vol_valid):.0f}] ų")
print(f" Density range: [{np.min(dens_valid):.1f}, {np.max(dens_valid):.1f}] g/cm³")
# Physical density range: most solids 0.5-25 g/cm³
extreme_dens = dens_valid[(dens_valid < 0.5) | (dens_valid > 25)]
if len(extreme_dens) > 100:
finding("MEDIUM", "scientific", "extreme_density",
f"{len(extreme_dens):,} outside [0.5, 25] g/cm³", "")
elif len(extreme_dens) > 0:
finding("LOW", "scientific", "extreme_density", f"{len(extreme_dens)} outliers", "")
else:
finding("PASS", "scientific", "density_plausible", "", "")
# Element distribution
print(f"\n--- Most Common Elements ---")
elem_counter = Counter()
for e in entries:
for el in e.get("elements", []):
elem_counter[el] += 1
for el, cnt in elem_counter.most_common(20):
print(f" {el:3s}: {cnt:,}")
# Element count distribution
print(f"\n--- Number of Elements ---")
nelem_counter = Counter()
for n_el in nelem:
nelem_counter[int(n_el)] += 1
for n_el, cnt in sorted(nelem_counter.items()):
print(f" {n_el} elements: {cnt:>7,}")
max_nelem = int(np.max(nelem))
if max_nelem > 6:
finding("LOW", "scientific", "high_element_count",
f"max elements = {max_nelem}", "")
# Space group distribution
print(f"\n--- Space Group Distribution ---")
sg_counter = Counter()
for s in sg:
if s is not None:
sg_counter[int(s)] += 1
for sg_num, cnt in sorted(sg_counter.most_common(30)):
print(f" SG {sg_num:3d}: {cnt:>7,}")
# Crystal system distribution
crystal_systems = {
"Triclinic": set(range(1, 3)), "Monoclinic": set(range(3, 16)),
"Orthorhombic": set(range(16, 75)), "Tetragonal": set(range(75, 143)),
"Trigonal": set(range(143, 168)), "Hexagonal": set(range(168, 195)),
"Cubic": set(range(195, 231)),
}
cs_counter = Counter()
for s in sg:
if s is not None:
for cs_name, sg_set in crystal_systems.items():
if int(s) in sg_set:
cs_counter[cs_name] += 1
break
print(f"\n Crystal System Distribution:")
total_cs = sum(cs_counter.values())
for cs_name, cnt in cs_counter.most_common():
print(f" {cs_name:14s}: {cnt:>7,} ({100*cnt/total_cs:.1f}%)")
print(f"\n--- Battery Relevance ---")
battery_entries = [e for e in entries if e.get("family") in
["layered_oxide", "polyanion", "sulfide_sse", "halide_sse",
"garnet", "perovskite_sse", "nasicon", "lisicon",
"antiperovskite_sse", "hydroborate_sse"]]
print(f" Battery-related: {len(battery_entries):,} ({100*len(battery_entries)/N:.1f}%)")
fe_oc = [e.get("formation_energy_per_atom") for e in battery_entries if e.get("formation_energy_per_atom") is not None]
if fe_oc:
print(f" Battery FE range: [{np.min(fe_oc):.2f}, {np.max(fe_oc):.2f}] eV/atom")
print(f"\n{'=' * 60}")
print(f" PHASE 7 SUMMARY")
print(f" CRITICAL: {severity_counts['CRITICAL']}")
print(f" HIGH: {severity_counts['HIGH']}")
print(f" MEDIUM: {severity_counts['MEDIUM']}")
print(f" LOW: {severity_counts['LOW']}")
print(f" PASS: {severity_counts['PASS']}")
print(f"{'=' * 60}")
report = {
"phase": "Phase 7: Scientific Audit",
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
"formation_energy": {
"N_valid": int(np.sum(~np.isnan(fe))),
"mean": float(np.nanmean(fe)),
"median": float(np.nanmedian(fe)),
"std": float(np.nanstd(fe)),
"min": float(np.nanmin(fe)),
"max": float(np.nanmax(fe)),
"p1": float(np.nanpercentile(fe, 1)),
"p5": float(np.nanpercentile(fe, 5)),
"p25": float(np.nanpercentile(fe, 25)),
"p50": float(np.nanpercentile(fe, 50)),
"p75": float(np.nanpercentile(fe, 75)),
"p95": float(np.nanpercentile(fe, 95)),
"p99": float(np.nanpercentile(fe, 99)),
"n_extreme_outliers": int(np.sum((fe < -6) | (fe > 4))),
},
"energy_above_hull": {
"N_valid": int(np.sum(~np.isnan(eah))),
"mean": float(np.nanmean(eah)),
"median": float(np.nanmedian(eah)),
"min": float(np.nanmin(eah)),
"max": float(np.nanmax(eah)),
"n_gt_1": int(np.sum(eah > 1)),
},
"band_gap": {
"N_valid": int(np.sum(~np.isnan(bg))),
"mean": float(np.nanmean(bg)),
"median": float(np.nanmedian(bg)),
"n_metal": int(np.sum(bg <= 0.1)),
},
"crystal_system": dict(cs_counter.most_common()),
"top_elements": {el: c for el, c in elem_counter.most_common(20)},
"n_elements_distribution": {str(k): v for k, v in sorted(nelem_counter.items())},
"findings": findings,
"summary": dict(severity_counts),
}
with open(OUT / "phase7_scientific_audit.json", "w") as f:
json.dump(report, f, indent=2)
print(f"\n Report: {OUT / 'phase7_scientific_audit.json'}")
if __name__ == "__main__":
main()
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