# -*- coding: utf-8 -*- """Run the precise 6x6 doped-Hubbard solver once per UNIQUE filling present in df_batch, then assign the material-specific d-wave amplitude A_d(delta) to every material. verified_score = N(E_F) * A_d(delta_material) (a physical, per-material quantity: it varies with doping via the d-wave dome). Cheap: only the distinct fillings are solved. """ import os, sys, json, glob, subprocess, time SOLVER = "/data/ginipick/hubbard_doped/pair_corr.py" CVD = os.environ.get("CUDA_VISIBLE_DEVICES", "3") OPT = int(os.environ.get("OSC_OPT", "200")) def solve(nup, ndn): out = f"/tmp/Abatch_{nup}_{ndn}.json" env = dict(os.environ, CUDA_VISIBLE_DEVICES=CVD) subprocess.run([sys.executable, "-u", SOLVER, "--size", "6x6", "--nup", str(nup), "--ndn", str(ndn), "--opt", str(OPT), "--meas", "20", "--Bmeas", "384", "--dwave", "0.15", "--out", out], env=env, cwd=os.path.dirname(SOLVER), timeout=900, check=False) d = json.load(open(out)) pds = {k: v for k, v in d.items() if k.startswith("Pd_") and not k.endswith("err")} if not pds: return None best = max(pds, key=lambda k: sum(int(c) for c in k.split("_")[1])) return float(pds[best]) def main(): rows = [json.load(open(f)) for f in glob.glob("df_batch/*.json")] uniq = sorted(set((r["solver_nup"], r["solver_ndn"]) for r in rows)) print(f"{len(rows)} materials, {len(uniq)} unique fillings to solve", flush=True) A = {} for nup, ndn in uniq: t0 = time.time() a = solve(nup, ndn) A[(nup, ndn)] = a print(f" Ne={nup+ndn} delta={1-(nup+ndn)/36.0:+.3f} A_d={a} ({time.time()-t0:.0f}s)", flush=True) os.makedirs("solved", exist_ok=True) for r in rows: a = A.get((r["solver_nup"], r["solver_ndn"])) r["A_dwave"] = round(a, 5) if a is not None else None json.dump(r, open("solved/" + r["material_id"] + ".json", "w"), indent=1) json.dump({f"{k[0]+k[1]}": v for k, v in A.items()}, open("solved/A_by_Ne.json", "w"), indent=1) print("done -> solved/", flush=True) if __name__ == "__main__": main()