#!/usr/bin/env python """Compare QE, HPRO, and ML (DeepH-E3) band structures for diamond. Processes both the pristine unit cell (UC) and pristine 2×2×2 supercell (SC). For each cell, runs DeepH-E3 inference and plots QE / HPRO / ML bands. Outputs: band_compare_uc_ml.png, band_compare_sc_ml.png Usage: python compare_bands.py [params.json] """ import glob import json import os import subprocess import sys import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from scipy.linalg import eigh SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, '..', '..', '1_data_prepare', 'data')) PARAMS_DEFAULT = os.path.abspath(os.path.join(SCRIPT_DIR, '..', '..', '1_data_prepare', 'params.json')) RESULTS_DIR = os.path.join(SCRIPT_DIR, 'results') def load_params(path=None): with open(path or PARAMS_DEFAULT) as f: return json.load(f) def find_latest_model(): """Return path to the latest trained model directory in results/.""" dirs = sorted(glob.glob(os.path.join(RESULTS_DIR, '*'))) dirs = [d for d in dirs if os.path.isdir(d) and os.path.exists(os.path.join(d, 'best_model.pkl'))] return dirs[-1] if dirs else None def infer_paths(cell_label): """Return (dataset_dir, graph_dir, output_dir, ini_path) for cell_label.""" base = os.path.join(SCRIPT_DIR, f'infer_{cell_label}') return ( os.path.join(base, 'dataset'), os.path.join(base, 'graph'), os.path.join(base, 'output'), os.path.join(base, 'eval.ini'), ) def build_infer_dataset(aodir, dataset_dir): """Create dataset_dir/00/ with per-file symlinks to aodir.""" dest = os.path.join(dataset_dir, '00') os.makedirs(dest, exist_ok=True) for fname in os.listdir(aodir): link = os.path.join(dest, fname) if not os.path.exists(link): os.symlink(os.path.join(aodir, fname), link) print(f' Inference dataset ready: {dest}') def write_eval_ini(ini_path, model_dir, dataset_dir, graph_dir, output_dir, dataset_name, params): """Write eval.ini for DeepH-E3 inference.""" t = params.get('hamiltonian', {}) ini = f"""; DeepH-E3 eval config — generated by compare_bands.py [basic] device = {t.get('device', 'cuda')} dtype = float trained_model_dir = {model_dir} output_dir = {output_dir} target = hamiltonian inference = False test_only = False [data] graph_dir = DFT_data_dir = processed_data_dir = {dataset_dir} save_graph_dir = {graph_dir} target_data = hamiltonian dataset_name = {dataset_name} get_overlap = False """ with open(ini_path, 'w') as f: f.write(ini) print(f' Written {ini_path}') def run_inference(ini_path, params): """Launch DeepH-E3 inference in the deeph conda environment.""" t = params.get('hamiltonian', {}) conda_env = t.get('conda_env', 'deeph') conda_base = params['paths']['conda_base'] deeph_e3_dir = t.get('deeph_e3_dir', '/home/apolyukhin/Development/DeepH-E3') launcher = os.path.join(SCRIPT_DIR, '_eval_launcher.py') with open(launcher, 'w') as f: f.write(f"""import sys, torch torch.serialization.add_safe_globals([slice]) try: from torch_geometric.data.data import DataEdgeAttr, DataTensorAttr from torch_geometric.data.storage import GlobalStorage torch.serialization.add_safe_globals([DataEdgeAttr, DataTensorAttr, GlobalStorage]) except ImportError: pass sys.path.insert(0, '{deeph_e3_dir}') from deephe3 import DeepHE3Kernel kernel = DeepHE3Kernel() kernel.eval('{ini_path}') """) activate = (f'source {conda_base}/etc/profile.d/conda.sh' f' && conda activate {conda_env}') print(' Running DeepH-E3 inference...') subprocess.run(['bash', '-c', f'{activate} && python {launcher}'], check=True) def load_kpath(): with open(os.path.join(DATA_DIR, 'bands', 'kpath.json')) as f: return json.load(f) def parse_bands_gnu(gnu_path): """Parse QE bands.dat.gnu → (nk, nbnd) array in eV.""" bands, block = [], [] with open(gnu_path) as f: for line in f: line = line.strip() if line: block.append(float(line.split()[1])) else: if block: bands.append(block) block = [] if block: bands.append(block) if not bands: raise FileNotFoundError(f'No data in {gnu_path}') return np.array(bands).T # (nk, nbnd) def compute_bands(aodir, h5_name, kpts_all, nbnd): """Diagonalize H(R) from h5_name in aodir along kpts_all. Uses the standard generalized eigenproblem eigh(Hk, Sk) matching the reference approach (add_qe_bands.py): hermitianize both Hk and Sk in k-space, then solve directly without Löwdin truncation. Returns (nk, nbnd) eigenvalues in eV. """ from HPRO.deephio import load_deeph_HS from HPRO.constants import hartree2ev matH = load_deeph_HS(aodir, h5_name, energy_unit=True) matS = load_deeph_HS(aodir, 'overlaps.h5', energy_unit=False) matS.hermitianize() nk = len(kpts_all) eigs_all = np.empty((nk, nbnd)) print(f' Diagonalizing {h5_name} at {nk} k-points...') for ik, kpt in enumerate(kpts_all): if ik % 50 == 0: print(f' k-point {ik}/{nk}') Hk = matH.r2k(kpt).toarray() Sk = matS.r2k(kpt).toarray() Hk = 0.5 * (Hk + Hk.conj().T) Sk = 0.5 * (Sk + Sk.conj().T) # hermitianize Sk in k-space (matches reference) eigs_k = np.sort(np.real(eigh(Hk, Sk, eigvals_only=True))) n = min(nbnd, len(eigs_k)) eigs_all[ik, :n] = eigs_k[:n] * hartree2ev if n < nbnd: eigs_all[ik, n:] = np.nan return eigs_all def plot_comparison(x, eigs_qe, eigs_hpro, eigs_ml, x_hs, labels, n_occ, title, outpath, n_cond=10, ewin=20.0): """Plot QE (blue), HPRO (red dashed), ML (green dotted), all VBM-aligned. MAE is reported separately for occupied bands and the n_cond lowest conduction bands. Only bands passing through [-ewin, +ewin] eV relative to VBM are plotted (avoids unphysical high-energy AO-basis artifacts). """ vbm_qe = np.max(eigs_qe[:, :n_occ]) vbm_hpro = np.max(eigs_hpro[:, :n_occ]) vbm_ml = np.max(eigs_ml[:, :n_occ]) nbnd_all = min(eigs_qe.shape[1], eigs_hpro.shape[1], eigs_ml.shape[1]) eq = eigs_qe[:, :nbnd_all] - vbm_qe eh = eigs_hpro[:, :nbnd_all] - vbm_hpro em = eigs_ml[:, :nbnd_all] - vbm_ml # MAE only over occupied + n_cond lowest conduction bands n_cmp = min(n_occ + n_cond, nbnd_all) def _mae(a, b): d = np.abs(a - b) return np.nanmean(d) mae_hpro_occ = _mae(eq[:, :n_occ], eh[:, :n_occ]) mae_ml_occ = _mae(eq[:, :n_occ], em[:, :n_occ]) mae_hpro_cond = _mae(eq[:, n_occ:n_cmp], eh[:, n_occ:n_cmp]) if n_cmp > n_occ else 0.0 mae_ml_cond = _mae(eq[:, n_occ:n_cmp], em[:, n_occ:n_cmp]) if n_cmp > n_occ else 0.0 print(f' MAE (occ) HPRO: {mae_hpro_occ*1000:.1f} meV ML: {mae_ml_occ*1000:.1f} meV') print(f' MAE (cond) HPRO: {mae_hpro_cond*1000:.1f} meV ML: {mae_ml_cond*1000:.1f} meV') fig, ax = plt.subplots(figsize=(7, 5)) for ib in range(nbnd_all): bq, bh, bm = eq[:, ib], eh[:, ib], em[:, ib] in_win = np.any((bq > -ewin) & (bq < ewin)) if not in_win: continue ax.plot(x, bq, 'b-', lw=1.0, alpha=0.7, label='QE' if ib == 0 else '') ax.plot(x, bh, 'r--', lw=0.9, alpha=0.7, label='HPRO' if ib == 0 else '') ax.plot(x, bm, 'g:', lw=1.0, alpha=0.8, label='ML' if ib == 0 else '') for xv in x_hs: ax.axvline(xv, color='k', lw=0.8, ls='--') ax.axhline(0, color='k', lw=0.5, ls=':') ax.set_xticks(x_hs) ax.set_xticklabels(labels, fontsize=11) ax.set_ylabel('Energy (eV)', fontsize=11) ax.set_xlim(x[0], x[-1]) ax.set_ylim(-ewin, ewin) ax.set_title( title + f'\nMAE occ: HPRO={mae_hpro_occ*1000:.1f} meV ML={mae_ml_occ*1000:.1f} meV' + f' | cond: HPRO={mae_hpro_cond*1000:.1f} meV ML={mae_ml_cond*1000:.1f} meV', fontsize=9) ax.legend(fontsize=9) fig.tight_layout() fig.savefig(outpath, dpi=200) plt.close(fig) print(f' Saved: {outpath}') def process_cell(cell_label, model_dir, kpath, params): """Run inference + plot for one cell (uc or sc).""" rec = params['reconstruction'] if cell_label == 'uc': nbnd = rec['nbnd'] n_occ = 4 # 4 valence electrons in the UC else: nbnd = rec.get('nbnd_sc', rec['nbnd']) n_occ = 4 * 8 # 4 valence e⁻ × 8 UC per 2×2×2 SC bands_dir = os.path.join(DATA_DIR, 'bands', cell_label) aodir = os.path.join(bands_dir, 'reconstruction', 'aohamiltonian') gnu = os.path.join(bands_dir, 'scf', 'bands.dat.gnu') if not os.path.exists(os.path.join(aodir, 'hamiltonians.h5')): print(f'[{cell_label}] hamiltonians.h5 not found, skipping') return if not os.path.exists(gnu): print(f'[{cell_label}] bands.dat.gnu not found, skipping') return dataset_dir, graph_dir, output_dir, ini_path = infer_paths(cell_label) pred_h5 = os.path.join(output_dir, '00', 'hamiltonians_pred.h5') pred_link = os.path.join(dataset_dir, '00', 'hamiltonians_pred.h5') if not os.path.exists(pred_h5): print(f'[{cell_label}] Step 1: Building inference dataset...') build_infer_dataset(aodir, dataset_dir) os.makedirs(graph_dir, exist_ok=True) os.makedirs(output_dir, exist_ok=True) print(f'[{cell_label}] Step 2: Writing eval.ini...') write_eval_ini(ini_path, model_dir, dataset_dir, graph_dir, output_dir, dataset_name=f'diamond_qe_e3_pristine_{cell_label}', params=params) print(f'[{cell_label}] Step 3: Running DeepH-E3 inference...') run_inference(ini_path, params) else: print(f'[{cell_label}] Using cached prediction: {pred_h5}') if not os.path.exists(pred_link): os.symlink(pred_h5, pred_link) kpts_all = np.array(kpath['kpts_all']) x_ref = np.array(kpath['x']) x_hs = kpath['x_hs'] labels = kpath['labels'] print(f'\n[{cell_label}] Loading QE bands...') eigs_qe = parse_bands_gnu(gnu) print(f'[{cell_label}] Computing HPRO bands...') eigs_hpro = compute_bands(aodir, 'hamiltonians.h5', kpts_all, nbnd) print(f'[{cell_label}] Computing ML bands...') eigs_ml = compute_bands(dataset_dir + '/00', 'hamiltonians_pred.h5', kpts_all, nbnd) outpath = os.path.join(SCRIPT_DIR, f'band_compare_{cell_label}_ml.png') cell_title = 'UC' if cell_label == 'uc' else 'SC (2×2×2)' print(f'[{cell_label}] Plotting...') plot_comparison(x_ref, eigs_qe, eigs_hpro, eigs_ml, x_hs, labels, n_occ, title=f'Diamond {cell_title}: QE vs HPRO vs ML (DeepH-E3)', outpath=outpath) def main(): params_path = next((a for a in sys.argv[1:] if not a.startswith('--')), None) params = load_params(params_path) model_dir = find_latest_model() if model_dir is None: print('ERROR: No trained model found in results/. Run train_ham.py first.') sys.exit(1) print(f'Using model: {os.path.basename(model_dir)}\n') kpath = load_kpath() for cell_label in ('uc', 'sc'): print(f'{"="*50}') print(f'Processing {cell_label.upper()}') print(f'{"="*50}') process_cell(cell_label, model_dir, kpath, params) print('\ncompare_bands.py done.') if __name__ == '__main__': main()