"""Integrate experimental Li solid-electrolyte conductivity data. Two separate, independently curated databases are supported: 1. **Hargreaves et al. 2023** — npj Computational Materials ~820 entries, 403 compositions, 214 sources https://doi.org/10.1038/s41524-023-01137-3 2. **OBELiX (Therrien et al. 2025, NRC-Mila)** ~599 entries, curated with leakage-resistant splits pip install obelix-data https://github.com/nrc-mila/OBELiX These are complementary — not duplicates — and are tracked as two separate provenance sources with distinct citations. Usage: # Hargreaves 2023 python scripts/integrate_experimental_data.py --ransom-path path/to/ransom2023.csv # OBELiX via pip package python scripts/integrate_experimental_data.py --obelix # Both python scripts/integrate_experimental_data.py --ransom-path ... --obelix # Dry run python scripts/integrate_experimental_data.py --dry-run """ import json, os, sys, time, argparse, csv, io, re, subprocess from pathlib import Path from collections import defaultdict import numpy as np import pandas as pd import warnings warnings.filterwarnings("ignore") WIDTH = 60 RANSOM_URLS = [ "https://raw.githubusercontent.com/nrc-cnrc/ransom2023-conductivity/main/data/conductivity_database.csv", ] HARGREAVES_DOI = "https://doi.org/10.1038/s41524-022-00951-z" OBELIX_DOI = "https://github.com/nrc-mila/OBELiX" def parse_formula(formula): parts = re.findall(r'([A-Z][a-z]*)(\d*\.?\d*)', formula) return {el: float(cnt) if cnt else 1.0 for el, cnt in parts} def formula_similarity(f1, f2): d1 = parse_formula(f1) d2 = parse_formula(f2) if set(d1.keys()) != set(d2.keys()): return False total1, total2 = sum(d1.values()), sum(d2.values()) for el in d1: r1 = d1[el] / total1 r2 = d2[el] / total2 if abs(r1 - r2) > 0.05: return False return True def try_fetch_ransom(): """Try to download Hargreaves 2023 database.""" import urllib.request for url in RANSOM_URLS: try: req = urllib.request.Request(url, headers={"User-Agent": "Scandium-Labs/1.0"}) with urllib.request.urlopen(req, timeout=30) as resp: data = resp.read().decode("utf-8") print(f" Downloaded {len(data):,} bytes") return data except Exception as e: print(f" Failed: {str(e)[:80]}") return None def try_fetch_obelix_package(): """Try to install obelix-data package and load data.""" try: import obelix ob = obelix.OBELiX(data_path="/tmp/obelix_rawdata", no_cifs=True) n = len(ob.dataframe) print(f" OBELiX package loaded: {n} entries") return ob except ImportError: print(" obelix-data not installed. Attempting pip install...") result = subprocess.run( [sys.executable, "-m", "pip", "install", "obelix-data"], capture_output=True, text=True, timeout=60 ) if result.returncode == 0: try: import obelix ob = obelix.OBELiX(data_path="/tmp/obelix_rawdata", no_cifs=True) n = len(ob.dataframe) print(f" OBELiX installed and loaded: {n} entries") return ob except Exception as e: print(f" Load failed after install: {e}") return None else: print(f" Install failed: {result.stderr[-200:]}") return None def parse_ransom_csv(csv_data): """Parse Hargreaves 2023 CSV into entry dicts.""" reader = csv.DictReader(io.StringIO(csv_data)) entries = [] for i, row in enumerate(reader): entry = { "source": "Hargreaves2023", "source_id": f"Hargreaves2023-{i:04d}", "is_experimental": True, "experimental_database": "Hargreaves2023", "provenance": { "source": "Hargreaves2023", "source_id": f"Hargreaves2023-{i:04d}", "doi": HARGREAVES_DOI, "integrated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), }, } formula = row.get("Formula", row.get("formula", "")).strip() if formula: entry["formula"] = formula entry["structured_formula"] = formula entry["elements"] = list(parse_formula(formula).keys()) entry["carrier_elements"] = ["Li"] for field in ["Conductivity_S_cm", "conductivity_S_cm", "Conductivity (S/cm)"]: val = row.get(field, "").strip() if val: try: entry["conductivity_S_cm"] = float(val) except ValueError: pass for field in ["Ea_eV", "activation_energy_eV", "Activation energy (eV)"]: val = row.get(field, "").strip() if val: try: entry["activation_energy_eV"] = float(val) except ValueError: pass for field in ["Temperature_K", "temperature_K", "Temperature (K)"]: val = row.get(field, "").strip() if val: try: entry["temperature_K"] = float(val) except ValueError: pass ref = row.get("Reference", row.get("reference", "")).strip() if ref: entry["reference"] = ref entry["provenance"]["experimental_reference"] = ref entries.append(entry) return entries def parse_obelix_via_package(obelix_obj): """Parse OBELiX data via pandas DataFrame.""" entries = [] try: df = obelix_obj.dataframe for idx, row in df.iterrows(): formula = str(row.get("Reduced Composition", "")) true_comp = str(row.get("True Composition", "")) conductivity = row.get("Ionic conductivity (S cm-1)") doi = str(row.get("DOI", "")) family = str(row.get("Family", "")) icsd = row.get("ICSD ID") sg = str(row.get("Space group", "")) entry = { "source": "OBELiX", "source_id": f"OBELiX-{idx}", "is_experimental": True, "experimental_database": "OBELiX_Therrien2025", "formula": formula, "structured_formula": true_comp if (true_comp and true_comp != "nan") else formula, "elements": list(parse_formula(formula).keys()) if formula else [], "carrier_elements": ["Li"], "conductivity_S_cm": float(conductivity) if pd.notna(conductivity) else None, "space_group": sg if sg != "nan" else "", "sse_family": family if family != "nan" else "", "reference": doi if doi != "nan" else "", "provenance": { "source": "OBELiX_Therrien2025", "source_id": f"OBELiX-{idx}", "doi": "https://github.com/nrc-mila/OBELiX", "icsd_id": str(icsd) if pd.notna(icsd) else "", "integrated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), }, } entries.append(entry) except Exception as e: print(f" OBELiX DataFrame parse error: {e}") return entries def cross_reference_and_add(exp_entries, all_dataset_entries): """Cross-reference experimental entries with the existing dataset.""" formula_index = defaultdict(list) for e in all_dataset_entries: sf = e.get("structured_formula", e.get("formula", "")) formula_index[sf].append(e) matched = 0 unmatched = 0 conductivity_added = 0 new_entries = [] for exp_e in exp_entries: exp_formula = exp_e.get("formula", "") matched_entries = formula_index.get(exp_formula, []) if not matched_entries: for sf, existing in formula_index.items(): if formula_similarity(exp_formula, sf): matched_entries = existing break db_name = exp_e.get("experimental_database", "unknown") if matched_entries: matched += 1 for existing_e in matched_entries: if "ssb_screening" not in existing_e: existing_e["ssb_screening"] = {} cond = exp_e.get("conductivity_S_cm") ea = exp_e.get("activation_energy_eV") if cond is not None: existing_e["ssb_screening"]["estimated_ionic_conductivity_S_cm"] = cond existing_e["ssb_screening"]["conductivity_source"] = f"experimental_{db_name}" conductivity_added += 1 if ea is not None: existing_e["ssb_screening"]["experimental_activation_energy_eV"] = ea existing_e["is_experimental"] = True if "provenance" not in existing_e: existing_e["provenance"] = {} existing_e["provenance"]["experimental_confirmed"] = True existing_e["provenance"]["experimental_database"] = db_name existing_e["provenance"]["experimental_reference"] = exp_e.get("reference", "") else: unmatched += 1 new_entry = { "source": exp_e.get("source", "experimental"), "source_id": exp_e.get("source_id", f"exp-{unmatched}"), "formula": exp_formula, "structured_formula": exp_formula, "elements": exp_e.get("elements", []), "nsites": len(exp_e.get("elements", [])), "band_gap": None, "formation_energy_per_atom": None, "energy_above_hull": None, "is_experimental": True, "families": ["experimental_SSE"], "sse_family": "experimental", "mobile_ion": "Li", "carrier_elements": ["Li"], "tier": "experimental_gold", "quality_score": 95, "quality_flags": ["experimental_data", "has_conductivity"], "ssb_screening": { "estimated_ionic_conductivity_S_cm": exp_e.get("conductivity_S_cm"), "conductivity_source": f"experimental_{db_name}", "experimental_activation_energy_eV": exp_e.get("activation_energy_eV"), "measurement_temperature_K": exp_e.get("temperature_K"), "mobile_ion": "Li", "sse_family": "experimental", "gates_passed": ["experimental"], "sse_candidate_score": 100, }, "provenance": exp_e.get("provenance", {}), "license": "CC-BY-4.0", } new_entries.append(new_entry) return matched, unmatched, conductivity_added, new_entries def main(): parser = argparse.ArgumentParser(description="Integrate experimental conductivity data") parser.add_argument("--ransom-path", type=str, default=None, help="Path to Hargreaves 2023 CSV file") parser.add_argument("--obelix", action="store_true", help="Try to load OBELiX via obelix-data package") parser.add_argument("--dry-run", action="store_true") parser.add_argument("--cross-ref-only", action="store_true") args = parser.parse_args() if not args.ransom_path and not args.obelix: print("Specify at least one data source:") print(" --ransom-path Hargreaves et al. 2023 database") print(" --obelix OBELiX via obelix-data package") sys.exit(1) BASE_DIR = Path(__file__).resolve().parent.parent DATASET_PATH = BASE_DIR / "dataset" print("=" * WIDTH) print(" EXPERIMENTAL DATA INTEGRATION") print("=" * WIDTH) all_experimental = [] # --- Hargreaves 2023 --- if args.ransom_path: source_label = "Hargreaves et al. 2023 (npj Comput. Mater.)" print(f"\n [{source_label}]") ransom_data = None path = Path(args.ransom_path) if path.exists(): with open(path) as f: ransom_data = f.read() print(f" Loaded from {path}") else: print(f" File not found: {path}") print(" Attempting download...") ransom_data = try_fetch_ransom() if ransom_data: entries = parse_ransom_csv(ransom_data) print(f" Parsed {len(entries):,} entries") for e in entries: e["experimental_database"] = "Hargreaves2023" all_experimental.extend(entries) with_cond = sum(1 for e in entries if e.get("conductivity_S_cm") is not None) with_ea = sum(1 for e in entries if e.get("activation_energy_eV") is not None) print(f" With conductivity: {with_cond}") print(f" With activation energy: {with_ea}") else: print(f" Could not load Hargreaves 2023 data.") print(f" Download manually from: {HARGREAVES_DOI}") # --- OBELiX Therrien 2025 --- if args.obelix: source_label = "OBELiX (Therrien et al. 2025, NRC-Mila)" print(f"\n [{source_label}]") print(" Attempting obelix-data package...") ob_data = try_fetch_obelix_package() if ob_data is not None: entries = parse_obelix_via_package(ob_data) print(f" Parsed {len(entries):,} entries") for e in entries: e["experimental_database"] = "OBELiX_Therrien2025" all_experimental.extend(entries) with_cond = sum(1 for e in entries if e.get("conductivity_S_cm") is not None) with_ea = sum(1 for e in entries if e.get("activation_energy_eV") is not None) print(f" With conductivity: {with_cond}") print(f" With activation energy: {with_ea}") else: print(f" Could not load OBELiX via package.") print(f" Try: pip install obelix-data") print(f" Or: https://github.com/nrc-mila/OBELiX") if not all_experimental: print("\n No experimental data loaded. Nothing to integrate.") sys.exit(1) # --- Cross-reference with existing dataset --- print(f"\n Loading Scandium-Dataset...") t0 = time.time() with open(DATASET_PATH / "entries_final_v3.json") as f: all_entries = json.load(f) print(f" {len(all_entries):,} entries ({time.time()-t0:.1f}s)") print(f"\n{'─' * WIDTH}") print(" Cross-referencing...") print(f"{'─' * WIDTH}") matched, unmatched, conductivity_added, new_entries = cross_reference_and_add( all_experimental, all_entries ) print(f"\n Results:") print(f" Matched existing entries: {matched}") print(f" Unmatched (new compositions): {unmatched}") print(f" Conductivity labels added: {conductivity_added}") print(f" New experimental entries: {len(new_entries)}") if new_entries: cond_entries = [(e.get("formula", "?"), e.get("ssb_screening", {}).get("estimated_ionic_conductivity_S_cm")) for e in new_entries if e.get("ssb_screening", {}).get("estimated_ionic_conductivity_S_cm")] for formula, cond in sorted(cond_entries, key=lambda x: -abs(x[1] or 0))[:5]: if cond: print(f" {formula:30s} σ={cond:.2e} S/cm") if not args.dry_run: if new_entries: all_entries.extend(new_entries) print(f"\n Added {len(new_entries):,} experimental entries") output_path = DATASET_PATH / "entries_final_v3.json" print(f" Writing to {output_path}...") t_write = time.time() with open(output_path, "w") as f: json.dump(all_entries, f) print(f" Done ({time.time()-t_write:.1f}s)") experimental_count = sum(1 for e in all_entries if e.get("is_experimental")) with_conductivity_total = sum( 1 for e in all_entries if e.get("ssb_screening", {}).get("estimated_ionic_conductivity_S_cm") ) print(f"\n{'─' * WIDTH}") print(" INTEGRATION SUMMARY") print(f"{'─' * WIDTH}") db_sources = set(e.get("experimental_database", "unknown") for e in all_experimental) for db in sorted(db_sources): count = sum(1 for e in all_experimental if e.get("experimental_database") == db) print(f" {db}: {count} entries") print(f" Total experimental entries in dataset: {experimental_count}") print(f" Entries with conductivity labels: {with_conductivity_total}") else: print(f"\n (dry-run)") print("=" * WIDTH) if __name__ == "__main__": main()