[WIP] Add diamond example dataset (all stages) (multi-commit 035430284dc329b6018851d151e26e96cf54512d40b35bfc421cf66e039ac242)

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/VSC filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/charge-density.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc1.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc10.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc100.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc101.dat filter=lfs diff=lfs merge=lfs -text
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+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc105.dat filter=lfs diff=lfs merge=lfs -text
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+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc107.dat filter=lfs diff=lfs merge=lfs -text
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+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc109.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc11.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc110.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc111.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc112.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc113.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc114.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc115.dat filter=lfs diff=lfs merge=lfs -text
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+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc120.dat filter=lfs diff=lfs merge=lfs -text
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+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc122.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc123.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc124.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc125.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc126.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc127.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc128.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc129.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc13.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc130.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc131.dat filter=lfs diff=lfs merge=lfs -text
+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc132.dat filter=lfs diff=lfs merge=lfs -text
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+example/diamond/1_data_prepare/data/bands/sc/scf/diamond.save/wfc134.dat filter=lfs diff=lfs merge=lfs -text

example/diamond/1_data_prepare/README.md

@@ -0,0 +1,51 @@
+# Diamond – Step 1: Data Preparation
+
+Generate displaced supercell configurations, run QE DFT, transform to AO basis,
+and verify band structure reconstruction.
+
+## Prerequisites
+
+1. **QE pseudopotential**: Place `C.upf` in `../pseudos/`
+2. **Siesta pseudopotential**: Place `C.psml` (or appropriate file) in `../aobasis/`
+3. **QE on PATH**: Source `~/scripts/tools/qe.sh` or set manually
+4. **Conda environments**: `hpro` (HPRO), `deeph` (DeepH)
+
+## Workflow
+
+```bash
+# 1. Create input files and directory structure
+python prepare.py
+
+# 2. Run QE calculations (SCF + pw2bgw + bands)
+python run.py
+
+# 3. Transform Hamiltonians to AO basis via HPRO
+python reconstruct.py
+
+# 4. Compare QE and reconstructed band structures
+python compare_bands.py
+```
+
+## Output
+
+```
+data/
+  disp-01/ .. disp-50/    displaced 2×2×2 supercell SCF + reconstruction
+  bands/
+    uc/                   pristine unit cell bands
+    sc/                   pristine 2×2×2 supercell bands
+    kpath.json            k-path used for band calculations
+band_compare_uc.png       QE vs HPRO comparison for unit cell
+band_compare_sc.png       QE vs HPRO comparison for supercell
+```
+
+## Parameters (params.json)
+
+| Key | Description |
+|-----|-------------|
+| `supercell_size` | Supercell dimensions (default 2×2×2) |
+| `n_displacements` | Number of displaced configs (default 50) |
+| `displacement_groups` | Amplitude groups in Angstrom |
+| `reconstruction.method` | `"original"` (real-space H(R)) |
+| `reconstruction.nbnd` | Bands to include in reconstruction comparison |
+| `cluster.run_sh` | Path to cluster submission script (null = local) |

example/diamond/1_data_prepare/compare_bands.py

@@ -0,0 +1,186 @@
+#!/usr/bin/env python
+"""
+Compare QE band structure with HPRO real-space reconstruction for diamond.
+
+Reads:
+  - data/bands/kpath.json              (k-path from prepare.py)
+  - data/bands/{uc,sc}/scf/bands.dat.gnu  (QE eigenvalues from bands.x)
+  - data/bands/{uc,sc}/reconstruction/aohamiltonian/  (HPRO H(R))
+
+Produces band comparison plots: band_compare_uc.png and band_compare_sc.png
+
+Usage: python compare_bands.py [params.json]
+"""
+import json
+import os
+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__))
+
+
+def load_params(path=None):
+    if path is None:
+        path = os.path.join(SCRIPT_DIR, 'params.json')
+    with open(path) as f:
+        return json.load(f)
+
+
+def load_kpath(data_dir):
+    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: blocks separated by blank lines.
+
+    Each block corresponds to one band; each line is 'k_dist  eigenvalue'.
+    Returns eigs (nk, nbnd) in eV (same units as bands.x output).
+    """
+    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 found in {gnu_path}")
+    return np.array(bands).T  # (nk, nbnd)
+
+
+def compute_hpro_bands(aodir, kpts_all, x_arr, nbnd):
+    """Compute band structure from HPRO H(R) via direct Fourier transform.
+
+    Uses load_deeph_HS + scipy.eigh for each k-point.
+    Hermitianizes H(k) (not H(R)) before diagonalizing.
+
+    Returns:
+      eigs   (nk, nbnd) eigenvalues in eV, aligned to kpts_all
+    """
+    from HPRO.deephio import load_deeph_HS
+    from HPRO.constants import hartree2ev
+
+    matH = load_deeph_HS(aodir, 'hamiltonians.h5', energy_unit=True)
+    matS = load_deeph_HS(aodir, 'overlaps.h5', energy_unit=False)
+    matS.hermitianize()  # S(R) is exact, hermitianize in real space
+
+    nk = len(kpts_all)
+    eigs_all = np.empty((nk, nbnd))
+
+    print(f"  Diagonalizing 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)  # hermitianize H(k) only
+        eigs_k, _ = eigh(Hk, Sk)
+        eigs_all[ik] = eigs_k[:nbnd] * hartree2ev
+
+    return eigs_all
+
+
+def plot_comparison(x, eigs_qe, eigs_hpro, x_hs, labels, title, outpath):
+    """Plot QE vs HPRO band structures (both pre-aligned to their own VBM)."""
+    fig, ax = plt.subplots(figsize=(6, 5))
+
+    for ib in range(eigs_qe.shape[1]):
+        ax.plot(x, eigs_qe[:, ib], 'b-', lw=1.2, alpha=0.8,
+                label='QE' if ib == 0 else '')
+    for ib in range(eigs_hpro.shape[1]):
+        ax.plot(x, eigs_hpro[:, ib], 'r--', lw=1.0, alpha=0.8,
+                label='HPRO' 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])
+    emax = max(np.max(eigs_qe), np.max(eigs_hpro))
+    emin = min(np.min(eigs_qe), np.min(eigs_hpro))
+    ax.set_ylim(emin - 1, emax + 1)
+    ax.set_title(title, fontsize=11)
+    ax.legend(fontsize=10)
+    fig.tight_layout()
+    fig.savefig(outpath, dpi=200)
+    plt.close(fig)
+    print(f"  Saved: {outpath}")
+
+
+def main():
+    params_path = sys.argv[1] if len(sys.argv) > 1 else \
+        os.path.join(SCRIPT_DIR, 'params.json')
+    params = load_params(params_path)
+
+    data_dir = os.path.join(SCRIPT_DIR, 'data')
+    kpath = load_kpath(data_dir)
+
+    kpts_hs = np.array(kpath['kpts_hs'])
+    npts = kpath['npts']
+    labels = kpath['labels']
+    x_ref = np.array(kpath['x'])
+    x_hs = kpath['x_hs']
+
+    for cell_label in ('uc', 'sc'):
+        bands_dir = os.path.join(data_dir, 'bands', cell_label)
+        scf_dir = os.path.join(bands_dir, 'scf')
+        aodir = os.path.join(bands_dir, 'reconstruction', 'aohamiltonian')
+
+        if not os.path.exists(os.path.join(aodir, 'hamiltonians.h5')):
+            print(f"[{cell_label}] HPRO hamiltonians.h5 not found, "
+                  "run reconstruct.py first")
+            continue
+
+        gnu = os.path.join(scf_dir, 'bands.dat.gnu')
+        if not os.path.exists(gnu):
+            print(f"[{cell_label}] bands.dat.gnu not found ({gnu}), "
+                  "run run.py first")
+            continue
+
+        print(f"\n[{cell_label}] Loading QE bands from bands.dat.gnu...")
+        eigs_qe = parse_bands_gnu(gnu)
+        # VBM: highest occupied level
+        n_occ = 4 if cell_label == 'uc' else 4 * 8  # 4 electrons per UC
+
+        rec = params['reconstruction']
+        nbnd_param = rec.get('nbnd_sc', rec['nbnd']) if cell_label == 'sc' else rec['nbnd']
+
+        print(f"[{cell_label}] Computing HPRO bands...")
+        kpts_all = np.array(kpath['kpts_all'])
+        eigs_hpro = compute_hpro_bands(aodir, kpts_all, x_ref, nbnd_param)
+
+        # Align each source independently to its own VBM
+        nbnd_cmp = min(nbnd_param, eigs_qe.shape[1], eigs_hpro.shape[1])
+        vbm_qe = np.max(eigs_qe[:, :n_occ])
+        vbm_hpro = np.max(eigs_hpro[:, :n_occ])
+        eigs_qe_al = eigs_qe[:, :nbnd_cmp] - vbm_qe
+        eigs_hpro_al = eigs_hpro[:, :nbnd_cmp] - vbm_hpro
+
+        mae = np.mean(np.abs(eigs_qe_al - eigs_hpro_al))
+        print(f"[{cell_label}] MAE (first {nbnd_cmp} bands, VBM-aligned) = "
+              f"{mae*1000:.1f} meV")
+
+        outpath = os.path.join(SCRIPT_DIR, f'band_compare_{cell_label}.png')
+        title = f'Diamond {cell_label.upper()}: QE vs HPRO reconstruction'
+        plot_comparison(x_ref, eigs_qe_al, eigs_hpro_al,
+                        x_hs, labels, title, outpath)
+
+    print("\ncompare_bands.py done.")
+
+
+if __name__ == '__main__':
+    main()

example/diamond/1_data_prepare/data/bands/kpath.json

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+  ],
+  "npts": [
+    30,
+    30,
+    30,
+    30,
+    30,
+    1
+  ],
+  "labels": [
+    "G",
+    "X",
+    "W",
+    "K",
+    "G",
+    "L"
+  ],
+  "kpts_all": [
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+seed = 42
+multiprocessing = 0
+orbital = [{"6 6": [0, 0]}, {"6 6": [0, 1]}, {"6 6": [0, 2]}, {"6 6": [0, 3]}, {"6 6": [0, 4]}, {"6 6": [0, 5]}, {"6 6": [0, 6]}, {"6 6": [0, 7]}, {"6 6": [0, 8]}, {"6 6": [0, 9]}, {"6 6": [0, 10]}, {"6 6": [0, 11]}, {"6 6": [0, 12]}, {"6 6": [1, 0]}, {"6 6": [1, 1]}, {"6 6": [1, 2]}, {"6 6": [1, 3]}, {"6 6": [1, 4]}, {"6 6": [1, 5]}, {"6 6": [1, 6]}, {"6 6": [1, 7]}, {"6 6": [1, 8]}, {"6 6": [1, 9]}, {"6 6": [1, 10]}, {"6 6": [1, 11]}, {"6 6": [1, 12]}, {"6 6": [2, 0]}, {"6 6": [2, 1]}, {"6 6": [2, 2]}, {"6 6": [2, 3]}, {"6 6": [2, 4]}, {"6 6": [2, 5]}, {"6 6": [2, 6]}, {"6 6": [2, 7]}, {"6 6": [2, 8]}, {"6 6": [2, 9]}, {"6 6": [2, 10]}, {"6 6": [2, 11]}, {"6 6": [2, 12]}, {"6 6": [3, 0]}, {"6 6": [3, 1]}, {"6 6": [3, 2]}, {"6 6": [3, 3]}, {"6 6": [3, 4]}, {"6 6": [3, 5]}, {"6 6": [3, 6]}, {"6 6": [3, 7]}, {"6 6": [3, 8]}, {"6 6": [3, 9]}, {"6 6": [3, 10]}, {"6 6": [3, 11]}, {"6 6": [3, 12]}, {"6 6": [4, 0]}, {"6 6": [4, 1]}, {"6 6": [4, 2]}, {"6 6": [4, 3]}, {"6 6": [4, 4]}, {"6 6": [4, 5]}, {"6 6": [4, 6]}, {"6 6": [4, 7]}, {"6 6": [4, 8]}, {"6 6": [4, 9]}, {"6 6": [4, 10]}, {"6 6": [4, 11]}, {"6 6": [4, 12]}, {"6 6": [5, 0]}, {"6 6": [5, 1]}, {"6 6": [5, 2]}, {"6 6": [5, 3]}, {"6 6": [5, 4]}, {"6 6": [5, 5]}, {"6 6": [5, 6]}, {"6 6": [5, 7]}, {"6 6": [5, 8]}, {"6 6": [5, 9]}, {"6 6": [5, 10]}, {"6 6": [5, 11]}, {"6 6": [5, 12]}, {"6 6": [6, 0]}, {"6 6": [6, 1]}, {"6 6": [6, 2]}, {"6 6": [6, 3]}, {"6 6": [6, 4]}, {"6 6": [6, 5]}, {"6 6": [6, 6]}, {"6 6": [6, 7]}, {"6 6": [6, 8]}, {"6 6": [6, 9]}, {"6 6": [6, 10]}, {"6 6": [6, 11]}, {"6 6": [6, 12]}, {"6 6": [7, 0]}, {"6 6": [7, 1]}, {"6 6": [7, 2]}, {"6 6": [7, 3]}, {"6 6": [7, 4]}, {"6 6": [7, 5]}, {"6 6": [7, 6]}, {"6 6": [7, 7]}, {"6 6": [7, 8]}, {"6 6": [7, 9]}, {"6 6": [7, 10]}, {"6 6": [7, 11]}, {"6 6": [7, 12]}, {"6 6": [8, 0]}, {"6 6": [8, 1]}, {"6 6": [8, 2]}, {"6 6": [8, 3]}, {"6 6": [8, 4]}, {"6 6": [8, 5]}, {"6 6": [8, 6]}, {"6 6": [8, 7]}, {"6 6": [8, 8]}, {"6 6": [8, 9]}, {"6 6": [8, 10]}, {"6 6": [8, 11]}, {"6 6": [8, 12]}, {"6 6": [9, 0]}, {"6 6": [9, 1]}, {"6 6": [9, 2]}, {"6 6": [9, 3]}, {"6 6": [9, 4]}, {"6 6": [9, 5]}, {"6 6": [9, 6]}, {"6 6": [9, 7]}, {"6 6": [9, 8]}, {"6 6": [9, 9]}, {"6 6": [9, 10]}, {"6 6": [9, 11]}, {"6 6": [9, 12]}, {"6 6": [10, 0]}, {"6 6": [10, 1]}, {"6 6": [10, 2]}, {"6 6": [10, 3]}, {"6 6": [10, 4]}, {"6 6": [10, 5]}, {"6 6": [10, 6]}, {"6 6": [10, 7]}, {"6 6": [10, 8]}, {"6 6": [10, 9]}, {"6 6": [10, 10]}, {"6 6": [10, 11]}, {"6 6": [10, 12]}, {"6 6": [11, 0]}, {"6 6": [11, 1]}, {"6 6": [11, 2]}, {"6 6": [11, 3]}, {"6 6": [11, 4]}, {"6 6": [11, 5]}, {"6 6": [11, 6]}, {"6 6": [11, 7]}, {"6 6": [11, 8]}, {"6 6": [11, 9]}, {"6 6": [11, 10]}, {"6 6": [11, 11]}, {"6 6": [11, 12]}, {"6 6": [12, 0]}, {"6 6": [12, 1]}, {"6 6": [12, 2]}, {"6 6": [12, 3]}, {"6 6": [12, 4]}, {"6 6": [12, 5]}, {"6 6": [12, 6]}, {"6 6": [12, 7]}, {"6 6": [12, 8]}, {"6 6": [12, 9]}, {"6 6": [12, 10]}, {"6 6": [12, 11]}, {"6 6": [12, 12]}]
+o_component = H
+energy_component = summation
+max_element = -1
+statistics = False
+normalizer = False
+boxcox = False
+
+[graph]
+radius = -1.0
+max_num_nbr = 0
+create_from_dft = True
+if_lcmp_graph = True
+separate_onsite = False
+new_sp = False
+
+[train]
+epochs = 5000
+pretrained = 
+resume = 
+train_ratio = 0.6
+val_ratio = 0.2
+test_ratio = 0.2
+early_stopping_loss = 0.0
+early_stopping_loss_epoch = [0.000000, 500]
+revert_then_decay = True
+revert_threshold = 30
+revert_decay_epoch = [800, 2000, 3000, 4000]
+revert_decay_gamma = [0.4, 0.5, 0.5, 0.4]
+clip_grad = True
+clip_grad_value = 4.2
+switch_sgd = False
+switch_sgd_lr = 1e-4
+switch_sgd_epoch = -1
+
+[hyperparameter]
+batch_size = 1
+dtype = float32
+optimizer = adam
+learning_rate = 0.001
+lr_scheduler = 
+lr_milestones = []
+momentum = 0.9
+weight_decay = 0
+criterion = MaskMSELoss
+retain_edge_fea = True
+lambda_eij = 0.0
+lambda_ei = 0.1
+lambda_etot = 0.0
+
+[network]
+atom_fea_len = 64
+edge_fea_len = 128
+gauss_stop = 6.0
+num_l = 4
+aggr = add
+distance_expansion = GaussianBasis
+if_exp = True
+if_multiplelinear = False
+if_edge_update = True
+if_lcmp = True
+normalization = LayerNorm
+atom_update_net = PAINN
+trainable_gaussians = False
+type_affine = False
+

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/result.txt

@@ -0,0 +1,86 @@
+====== CONFIG ======
+[basic]
+graph_dir=/home/apolyukhin/scripts/ml/diamond-qe/deeph-data/graph
+save_dir=/home/apolyukhin/scripts/ml/diamond-qe/pristine-222/reconstruction/aohamiltonian/pred_ham_std
+raw_dir=/home/apolyukhin/scripts/ml/diamond-qe/deeph-data/preprocess
+dataset_name=diamond_qe
+only_get_graph=False
+interface=h5
+target=hamiltonian
+disable_cuda=True
+device=cpu
+num_threads=-1
+save_to_time_folder=False
+save_csv=True
+tb_writer=False
+seed=42
+multiprocessing=0
+orbital=[{"6 6": [0, 0]}, {"6 6": [0, 1]}, {"6 6": [0, 2]}, {"6 6": [0, 3]}, {"6 6": [0, 4]}, {"6 6": [0, 5]}, {"6 6": [0, 6]}, {"6 6": [0, 7]}, {"6 6": [0, 8]}, {"6 6": [0, 9]}, {"6 6": [0, 10]}, {"6 6": [0, 11]}, {"6 6": [0, 12]}, {"6 6": [1, 0]}, {"6 6": [1, 1]}, {"6 6": [1, 2]}, {"6 6": [1, 3]}, {"6 6": [1, 4]}, {"6 6": [1, 5]}, {"6 6": [1, 6]}, {"6 6": [1, 7]}, {"6 6": [1, 8]}, {"6 6": [1, 9]}, {"6 6": [1, 10]}, {"6 6": [1, 11]}, {"6 6": [1, 12]}, {"6 6": [2, 0]}, {"6 6": [2, 1]}, {"6 6": [2, 2]}, {"6 6": [2, 3]}, {"6 6": [2, 4]}, {"6 6": [2, 5]}, {"6 6": [2, 6]}, {"6 6": [2, 7]}, {"6 6": [2, 8]}, {"6 6": [2, 9]}, {"6 6": [2, 10]}, {"6 6": [2, 11]}, {"6 6": [2, 12]}, {"6 6": [3, 0]}, {"6 6": [3, 1]}, {"6 6": [3, 2]}, {"6 6": [3, 3]}, {"6 6": [3, 4]}, {"6 6": [3, 5]}, {"6 6": [3, 6]}, {"6 6": [3, 7]}, {"6 6": [3, 8]}, {"6 6": [3, 9]}, {"6 6": [3, 10]}, {"6 6": [3, 11]}, {"6 6": [3, 12]}, {"6 6": [4, 0]}, {"6 6": [4, 1]}, {"6 6": [4, 2]}, {"6 6": [4, 3]}, {"6 6": [4, 4]}, {"6 6": [4, 5]}, {"6 6": [4, 6]}, {"6 6": [4, 7]}, {"6 6": [4, 8]}, {"6 6": [4, 9]}, {"6 6": [4, 10]}, {"6 6": [4, 11]}, {"6 6": [4, 12]}, {"6 6": [5, 0]}, {"6 6": [5, 1]}, {"6 6": [5, 2]}, {"6 6": [5, 3]}, {"6 6": [5, 4]}, {"6 6": [5, 5]}, {"6 6": [5, 6]}, {"6 6": [5, 7]}, {"6 6": [5, 8]}, {"6 6": [5, 9]}, {"6 6": [5, 10]}, {"6 6": [5, 11]}, {"6 6": [5, 12]}, {"6 6": [6, 0]}, {"6 6": [6, 1]}, {"6 6": [6, 2]}, {"6 6": [6, 3]}, {"6 6": [6, 4]}, {"6 6": [6, 5]}, {"6 6": [6, 6]}, {"6 6": [6, 7]}, {"6 6": [6, 8]}, {"6 6": [6, 9]}, {"6 6": [6, 10]}, {"6 6": [6, 11]}, {"6 6": [6, 12]}, {"6 6": [7, 0]}, {"6 6": [7, 1]}, {"6 6": [7, 2]}, {"6 6": [7, 3]}, {"6 6": [7, 4]}, {"6 6": [7, 5]}, {"6 6": [7, 6]}, {"6 6": [7, 7]}, {"6 6": [7, 8]}, {"6 6": [7, 9]}, {"6 6": [7, 10]}, {"6 6": [7, 11]}, {"6 6": [7, 12]}, {"6 6": [8, 0]}, {"6 6": [8, 1]}, {"6 6": [8, 2]}, {"6 6": [8, 3]}, {"6 6": [8, 4]}, {"6 6": [8, 5]}, {"6 6": [8, 6]}, {"6 6": [8, 7]}, {"6 6": [8, 8]}, {"6 6": [8, 9]}, {"6 6": [8, 10]}, {"6 6": [8, 11]}, {"6 6": [8, 12]}, {"6 6": [9, 0]}, {"6 6": [9, 1]}, {"6 6": [9, 2]}, {"6 6": [9, 3]}, {"6 6": [9, 4]}, {"6 6": [9, 5]}, {"6 6": [9, 6]}, {"6 6": [9, 7]}, {"6 6": [9, 8]}, {"6 6": [9, 9]}, {"6 6": [9, 10]}, {"6 6": [9, 11]}, {"6 6": [9, 12]}, {"6 6": [10, 0]}, {"6 6": [10, 1]}, {"6 6": [10, 2]}, {"6 6": [10, 3]}, {"6 6": [10, 4]}, {"6 6": [10, 5]}, {"6 6": [10, 6]}, {"6 6": [10, 7]}, {"6 6": [10, 8]}, {"6 6": [10, 9]}, {"6 6": [10, 10]}, {"6 6": [10, 11]}, {"6 6": [10, 12]}, {"6 6": [11, 0]}, {"6 6": [11, 1]}, {"6 6": [11, 2]}, {"6 6": [11, 3]}, {"6 6": [11, 4]}, {"6 6": [11, 5]}, {"6 6": [11, 6]}, {"6 6": [11, 7]}, {"6 6": [11, 8]}, {"6 6": [11, 9]}, {"6 6": [11, 10]}, {"6 6": [11, 11]}, {"6 6": [11, 12]}, {"6 6": [12, 0]}, {"6 6": [12, 1]}, {"6 6": [12, 2]}, {"6 6": [12, 3]}, {"6 6": [12, 4]}, {"6 6": [12, 5]}, {"6 6": [12, 6]}, {"6 6": [12, 7]}, {"6 6": [12, 8]}, {"6 6": [12, 9]}, {"6 6": [12, 10]}, {"6 6": [12, 11]}, {"6 6": [12, 12]}]
+o_component=H
+energy_component=summation
+max_element=-1
+statistics=False
+normalizer=False
+boxcox=False
+
+[graph]
+radius=-1.0
+max_num_nbr=0
+create_from_dft=True
+if_lcmp_graph=True
+separate_onsite=False
+new_sp=False
+
+[train]
+epochs=5000
+pretrained=
+resume=
+train_ratio=0.6
+val_ratio=0.2
+test_ratio=0.2
+early_stopping_loss=0.0
+early_stopping_loss_epoch=[0.000000, 500]
+revert_then_decay=True
+revert_threshold=30
+revert_decay_epoch=[800, 2000, 3000, 4000]
+revert_decay_gamma=[0.4, 0.5, 0.5, 0.4]
+clip_grad=True
+clip_grad_value=4.2
+switch_sgd=False
+switch_sgd_lr=1e-4
+switch_sgd_epoch=-1
+
+[hyperparameter]
+batch_size=1
+dtype=float32
+optimizer=adam
+learning_rate=0.001
+lr_scheduler=
+lr_milestones=[]
+momentum=0.9
+weight_decay=0
+criterion=MaskMSELoss
+retain_edge_fea=True
+lambda_eij=0.0
+lambda_ei=0.1
+lambda_etot=0.0
+
+[network]
+atom_fea_len=64
+edge_fea_len=128
+gauss_stop=6.0
+num_l=4
+aggr=add
+distance_expansion=GaussianBasis
+if_exp=True
+if_multiplelinear=False
+if_edge_update=True
+if_lcmp=True
+normalization=LayerNorm
+atom_update_net=PAINN
+trainable_gaussians=False
+type_affine=False
+
+=> load best checkpoint (epoch 1070)
+=> Atomic types: [6], spinful: False, the number of atomic types: 1.
+Save processed graph to /home/apolyukhin/scripts/ml/diamond-qe/pristine-222/reconstruction/aohamiltonian/graph.pkl, cost 1.6528115272521973 seconds

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/__init__.py

@@ -0,0 +1,10 @@
+from .data import HData
+from .model import HGNN, ExpBernsteinBasis
+from .utils import print_args, Logger, MaskMSELoss, MaskMAELoss, write_ham_npz, write_ham, write_ham_h5, get_config, \
+    get_inference_config, get_preprocess_config
+from .graph import Collater, collate_fn, get_graph, load_orbital_types
+from .kernel import DeepHKernel
+from .preprocess import get_rc, OijLoad, GetEEiEij, abacus_parse, siesta_parse
+from .rotate import get_rh, rotate_back, Rotate, dtype_dict
+
+__version__ = "0.2.2"

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/data.py

@@ -0,0 +1,217 @@
+import warnings
+import os
+import time
+import tqdm
+
+from pymatgen.core.structure import Structure
+import numpy as np
+import torch
+from torch_geometric.data import InMemoryDataset
+from pathos.multiprocessing import ProcessingPool as Pool
+
+from .graph import get_graph
+
+
+class HData(InMemoryDataset):
+    def __init__(self, raw_data_dir: str, graph_dir: str, interface: str, target: str,
+                 dataset_name: str, multiprocessing: int, radius, max_num_nbr,
+                 num_l, max_element, create_from_DFT, if_lcmp_graph, separate_onsite, new_sp,
+                 default_dtype_torch, nums: int = None, transform=None, pre_transform=None, pre_filter=None):
+        """
+when interface == 'h5',
+raw_data_dir
+├── 00
+│     ├──rh.h5 / rdm.h5
+│     ├──rc.h5
+│     ├──element.dat
+│     ├──orbital_types.dat
+│     ├──site_positions.dat
+│     ├──lat.dat
+│     └──info.json
+├── 01
+│     ├──rh.h5 / rdm.h5
+│     ├──rc.h5
+│     ├──element.dat
+│     ├──orbital_types.dat
+│     ├──site_positions.dat
+│     ├──lat.dat
+│     └──info.json
+├── 02
+│     ├──rh.h5 / rdm.h5
+│     ├──rc.h5
+│     ├──element.dat
+│     ├──orbital_types.dat
+│     ├──site_positions.dat
+│     ├──lat.dat
+│     └──info.json
+├── ...
+        """
+        self.raw_data_dir = raw_data_dir
+        assert dataset_name.find('-') == -1, '"-" can not be included in the dataset name'
+        if create_from_DFT:
+            way_create_graph = 'FromDFT'
+        else:
+            way_create_graph = f'{radius}r{max_num_nbr}mn'
+        if if_lcmp_graph:
+            lcmp_str = f'{num_l}l'
+        else:
+            lcmp_str = 'WithoutLCMP'
+        if separate_onsite is True:
+            onsite_str = '-SeparateOnsite'
+        else:
+            onsite_str = ''
+        if new_sp:
+            new_sp_str = '-NewSP'
+        else:
+            new_sp_str = ''
+        if target == 'hamiltonian':
+            title = 'HGraph'
+        else:
+            raise ValueError('Unknown prediction target: {}'.format(target))
+        graph_file_name = f'{title}-{interface}-{dataset_name}-{lcmp_str}-{way_create_graph}{onsite_str}{new_sp_str}.pkl'
+        self.data_file = os.path.join(graph_dir, graph_file_name)
+        os.makedirs(graph_dir, exist_ok=True)
+        self.data, self.slices = None, None
+        self.interface = interface
+        self.target = target
+        self.dataset_name = dataset_name
+        self.multiprocessing = multiprocessing
+        self.radius = radius
+        self.max_num_nbr = max_num_nbr
+        self.num_l = num_l
+        self.create_from_DFT = create_from_DFT
+        self.if_lcmp_graph = if_lcmp_graph
+        self.separate_onsite = separate_onsite
+        self.new_sp = new_sp
+        self.default_dtype_torch = default_dtype_torch
+
+        self.nums = nums
+        self.transform = transform
+        self.pre_transform = pre_transform
+        self.pre_filter = pre_filter
+        self.__indices__ = None
+        self.__data_list__ = None
+        self._indices = None
+        self._data_list = None
+
+        print(f'Graph data file: {graph_file_name}')
+        if os.path.exists(self.data_file):
+            print('Use existing graph data file')
+        else:
+            print('Process new data file......')
+            self.process()
+        begin = time.time()
+        try:
+            loaded_data = torch.load(self.data_file)
+        except AttributeError:
+            raise RuntimeError('Error in loading graph data file, try to delete it and generate the graph file with the current version of PyG')
+        if len(loaded_data) == 2:
+            warnings.warn('You are using the graph data file with an old version')
+            self.data, self.slices = loaded_data
+            self.info = {
+                "spinful": False,
+                "index_to_Z": torch.arange(max_element + 1),
+                "Z_to_index": torch.arange(max_element + 1),
+            }
+        elif len(loaded_data) == 3:
+            self.data, self.slices, tmp = loaded_data
+            if isinstance(tmp, dict):
+                self.info = tmp
+                print(f"Atomic types: {self.info['index_to_Z'].tolist()}")
+            else:
+                warnings.warn('You are using an old version of the graph data file')
+                self.info = {
+                    "spinful": tmp,
+                    "index_to_Z": torch.arange(max_element + 1),
+                    "Z_to_index": torch.arange(max_element + 1),
+                }
+        print(f'Finish loading the processed {len(self)} structures (spinful: {self.info["spinful"]}, '
+              f'the number of atomic types: {len(self.info["index_to_Z"])}), cost {time.time() - begin:.0f} seconds')
+
+    def process_worker(self, folder, **kwargs):
+        stru_id = os.path.split(folder)[-1]
+
+        structure = Structure(np.loadtxt(os.path.join(folder, 'lat.dat')).T,
+                              np.loadtxt(os.path.join(folder, 'element.dat')),
+                              np.loadtxt(os.path.join(folder, 'site_positions.dat')).T,
+                              coords_are_cartesian=True,
+                              to_unit_cell=False)
+
+        cart_coords = torch.tensor(structure.cart_coords, dtype=self.default_dtype_torch)
+        frac_coords = torch.tensor(structure.frac_coords, dtype=self.default_dtype_torch)
+        numbers = torch.tensor(structure.atomic_numbers)
+        structure.lattice.matrix.setflags(write=True)
+        lattice = torch.tensor(structure.lattice.matrix, dtype=self.default_dtype_torch)
+        if self.target == 'E_ij':
+            huge_structure = True
+        else:
+            huge_structure = False
+        return get_graph(cart_coords, frac_coords, numbers, stru_id, r=self.radius, max_num_nbr=self.max_num_nbr,
+                         numerical_tol=1e-8, lattice=lattice, default_dtype_torch=self.default_dtype_torch,
+                         tb_folder=folder, interface=self.interface, num_l=self.num_l,
+                         create_from_DFT=self.create_from_DFT, if_lcmp_graph=self.if_lcmp_graph,
+                         separate_onsite=self.separate_onsite,
+                         target=self.target, huge_structure=huge_structure, if_new_sp=self.new_sp, **kwargs)
+
+    def process(self):
+        begin = time.time()
+        folder_list = []
+        for root, dirs, files in os.walk(self.raw_data_dir):
+            if (self.interface == 'h5' and 'rc.h5' in files) or (
+                    self.interface == 'npz' and 'rc.npz' in files):
+                folder_list.append(root)
+        folder_list = sorted(folder_list)
+        folder_list = folder_list[: self.nums]
+        if self.dataset_name == 'graphene_450':
+            folder_list = folder_list[500:5000:10]
+        if self.dataset_name == 'graphene_1500':
+            folder_list = folder_list[500:5000:3]
+        if self.dataset_name == 'bp_bilayer':
+            folder_list = folder_list[:600]
+        assert len(folder_list) != 0, "Can not find any structure"
+        print('Found %d structures, have cost %d seconds' % (len(folder_list), time.time() - begin))
+
+        if self.multiprocessing == 0:
+            print(f'Use multiprocessing (nodes = num_processors x num_threads = 1 x {torch.get_num_threads()})')
+            data_list = [self.process_worker(folder) for folder in tqdm.tqdm(folder_list)]
+        else:
+            pool_dict = {} if self.multiprocessing < 0 else {'nodes': self.multiprocessing}
+            # BS (2023.06.06): 
+            # The keyword "num_threads" in kernel.py can be used to set the torch threads.
+            # The multiprocessing in the "process_worker" is in contradiction with the num_threads utilized in torch.
+            # To avoid this conflict, I limit the number of torch threads to one,
+            # and recover it when finishing the process_worker.
+            torch_num_threads = torch.get_num_threads()
+            torch.set_num_threads(1)
+
+            with Pool(**pool_dict) as pool:
+                nodes = pool.nodes
+                print(f'Use multiprocessing (nodes = num_processors x num_threads = {nodes} x {torch.get_num_threads()})')
+                data_list = list(tqdm.tqdm(pool.imap(self.process_worker, folder_list), total=len(folder_list)))
+            torch.set_num_threads(torch_num_threads)
+        print('Finish processing %d structures, have cost %d seconds' % (len(data_list), time.time() - begin))
+
+        if self.pre_filter is not None:
+            data_list = [d for d in data_list if self.pre_filter(d)]
+        if self.pre_transform is not None:
+            data_list = [self.pre_transform(d) for d in data_list]
+
+        index_to_Z, Z_to_index = self.element_statistics(data_list)
+        spinful = data_list[0].spinful
+        for d in data_list:
+            assert spinful == d.spinful
+
+        data, slices = self.collate(data_list)
+        torch.save((data, slices, dict(spinful=spinful, index_to_Z=index_to_Z, Z_to_index=Z_to_index)), self.data_file)
+        print('Finish saving %d structures to %s, have cost %d seconds' % (
+        len(data_list), self.data_file, time.time() - begin))
+
+    def element_statistics(self, data_list):
+        index_to_Z, inverse_indices = torch.unique(data_list[0].x, sorted=True, return_inverse=True)
+        Z_to_index = torch.full((100,), -1, dtype=torch.int64)
+        Z_to_index[index_to_Z] = torch.arange(len(index_to_Z))
+
+        for data in data_list:
+            data.x = Z_to_index[data.x]
+
+        return index_to_Z, Z_to_index

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/default.ini

@@ -0,0 +1,88 @@
+[basic]
+graph_dir = /your/own/path
+save_dir = /your/own/path
+raw_dir = /your/own/path
+dataset_name = your_own_name
+only_get_graph = False
+;choices = ['h5', 'npz']
+interface = h5
+target = hamiltonian
+disable_cuda = False
+device = cuda:0
+;-1 for cpu_count(logical=False) // torch.cuda.device_count()
+num_threads = -1
+save_to_time_folder = True
+save_csv = False
+tb_writer = True
+seed = 42
+multiprocessing = 0
+orbital = [{"6 6": [0, 0]}, {"6 6": [0, 1]}, {"6 6": [0, 2]}, {"6 6": [0, 3]}, {"6 6": [0, 4]}, {"6 6": [0, 5]}, {"6 6": [0, 6]}, {"6 6": [0, 7]}, {"6 6": [0, 8]}, {"6 6": [0, 9]}, {"6 6": [0, 10]}, {"6 6": [0, 11]}, {"6 6": [0, 12]}, {"6 6": [1, 0]}, {"6 6": [1, 1]}, {"6 6": [1, 2]}, {"6 6": [1, 3]}, {"6 6": [1, 4]}, {"6 6": [1, 5]}, {"6 6": [1, 6]}, {"6 6": [1, 7]}, {"6 6": [1, 8]}, {"6 6": [1, 9]}, {"6 6": [1, 10]}, {"6 6": [1, 11]}, {"6 6": [1, 12]}, {"6 6": [2, 0]}, {"6 6": [2, 1]}, {"6 6": [2, 2]}, {"6 6": [2, 3]}, {"6 6": [2, 4]}, {"6 6": [2, 5]}, {"6 6": [2, 6]}, {"6 6": [2, 7]}, {"6 6": [2, 8]}, {"6 6": [2, 9]}, {"6 6": [2, 10]}, {"6 6": [2, 11]}, {"6 6": [2, 12]}, {"6 6": [3, 0]}, {"6 6": [3, 1]}, {"6 6": [3, 2]}, {"6 6": [3, 3]}, {"6 6": [3, 4]}, {"6 6": [3, 5]}, {"6 6": [3, 6]}, {"6 6": [3, 7]}, {"6 6": [3, 8]}, {"6 6": [3, 9]}, {"6 6": [3, 10]}, {"6 6": [3, 11]}, {"6 6": [3, 12]}, {"6 6": [4, 0]}, {"6 6": [4, 1]}, {"6 6": [4, 2]}, {"6 6": [4, 3]}, {"6 6": [4, 4]}, {"6 6": [4, 5]}, {"6 6": [4, 6]}, {"6 6": [4, 7]}, {"6 6": [4, 8]}, {"6 6": [4, 9]}, {"6 6": [4, 10]}, {"6 6": [4, 11]}, {"6 6": [4, 12]}, {"6 6": [5, 0]}, {"6 6": [5, 1]}, {"6 6": [5, 2]}, {"6 6": [5, 3]}, {"6 6": [5, 4]}, {"6 6": [5, 5]}, {"6 6": [5, 6]}, {"6 6": [5, 7]}, {"6 6": [5, 8]}, {"6 6": [5, 9]}, {"6 6": [5, 10]}, {"6 6": [5, 11]}, {"6 6": [5, 12]}, {"6 6": [6, 0]}, {"6 6": [6, 1]}, {"6 6": [6, 2]}, {"6 6": [6, 3]}, {"6 6": [6, 4]}, {"6 6": [6, 5]}, {"6 6": [6, 6]}, {"6 6": [6, 7]}, {"6 6": [6, 8]}, {"6 6": [6, 9]}, {"6 6": [6, 10]}, {"6 6": [6, 11]}, {"6 6": [6, 12]}, {"6 6": [7, 0]}, {"6 6": [7, 1]}, {"6 6": [7, 2]}, {"6 6": [7, 3]}, {"6 6": [7, 4]}, {"6 6": [7, 5]}, {"6 6": [7, 6]}, {"6 6": [7, 7]}, {"6 6": [7, 8]}, {"6 6": [7, 9]}, {"6 6": [7, 10]}, {"6 6": [7, 11]}, {"6 6": [7, 12]}, {"6 6": [8, 0]}, {"6 6": [8, 1]}, {"6 6": [8, 2]}, {"6 6": [8, 3]}, {"6 6": [8, 4]}, {"6 6": [8, 5]}, {"6 6": [8, 6]}, {"6 6": [8, 7]}, {"6 6": [8, 8]}, {"6 6": [8, 9]}, {"6 6": [8, 10]}, {"6 6": [8, 11]}, {"6 6": [8, 12]}, {"6 6": [9, 0]}, {"6 6": [9, 1]}, {"6 6": [9, 2]}, {"6 6": [9, 3]}, {"6 6": [9, 4]}, {"6 6": [9, 5]}, {"6 6": [9, 6]}, {"6 6": [9, 7]}, {"6 6": [9, 8]}, {"6 6": [9, 9]}, {"6 6": [9, 10]}, {"6 6": [9, 11]}, {"6 6": [9, 12]}, {"6 6": [10, 0]}, {"6 6": [10, 1]}, {"6 6": [10, 2]}, {"6 6": [10, 3]}, {"6 6": [10, 4]}, {"6 6": [10, 5]}, {"6 6": [10, 6]}, {"6 6": [10, 7]}, {"6 6": [10, 8]}, {"6 6": [10, 9]}, {"6 6": [10, 10]}, {"6 6": [10, 11]}, {"6 6": [10, 12]}, {"6 6": [11, 0]}, {"6 6": [11, 1]}, {"6 6": [11, 2]}, {"6 6": [11, 3]}, {"6 6": [11, 4]}, {"6 6": [11, 5]}, {"6 6": [11, 6]}, {"6 6": [11, 7]}, {"6 6": [11, 8]}, {"6 6": [11, 9]}, {"6 6": [11, 10]}, {"6 6": [11, 11]}, {"6 6": [11, 12]}, {"6 6": [12, 0]}, {"6 6": [12, 1]}, {"6 6": [12, 2]}, {"6 6": [12, 3]}, {"6 6": [12, 4]}, {"6 6": [12, 5]}, {"6 6": [12, 6]}, {"6 6": [12, 7]}, {"6 6": [12, 8]}, {"6 6": [12, 9]}, {"6 6": [12, 10]}, {"6 6": [12, 11]}, {"6 6": [12, 12]}]
+O_component = H
+energy_component = summation
+max_element = -1
+statistics = False
+normalizer = False
+boxcox = False
+
+[graph]
+radius = -1.0
+max_num_nbr = 0
+create_from_DFT = True
+if_lcmp_graph = True
+separate_onsite = False
+new_sp = False
+
+[train]
+epochs = 4000
+pretrained =
+resume =
+train_ratio = 0.6
+val_ratio = 0.2
+test_ratio = 0.2
+early_stopping_loss = 0.0
+early_stopping_loss_epoch = [0.000000, 500]
+revert_then_decay = True
+revert_threshold = 30
+revert_decay_epoch = [500, 2000, 3000]
+revert_decay_gamma = [0.4, 0.5, 0.5]
+clip_grad = True
+clip_grad_value = 4.2
+switch_sgd = False
+switch_sgd_lr = 1e-4
+switch_sgd_epoch = -1
+
+[hyperparameter]
+batch_size = 3
+dtype = float32
+;choices = ['sgd', 'sgdm', 'adam', 'lbfgs']
+optimizer = adam
+;initial learning rate
+learning_rate = 0.001
+;choices = ['', 'MultiStepLR', 'ReduceLROnPlateau', 'CyclicLR']
+lr_scheduler =
+lr_milestones = []
+momentum = 0.9
+weight_decay = 0
+criterion = MaskMSELoss
+retain_edge_fea = True
+lambda_Eij = 0.0
+lambda_Ei = 0.1
+lambda_Etot = 0.0
+
+[network]
+atom_fea_len = 64
+edge_fea_len = 128
+gauss_stop = 6
+;The number of angular quantum numbers that spherical harmonic functions have
+num_l = 5
+aggr = add
+distance_expansion = GaussianBasis
+if_exp = True
+if_MultipleLinear = False
+if_edge_update = True
+if_lcmp = True
+normalization = LayerNorm
+;choices = ['CGConv', 'GAT', 'PAINN']
+atom_update_net = CGConv
+trainable_gaussians = False
+type_affine = False

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_HermNet/__init__.py

@@ -0,0 +1 @@
+from .rmnet import RBF, cosine_cutoff, ShiftedSoftplus, _eps

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_HermNet/license.txt

@@ -0,0 +1 @@
+The code in this folder was obtained from "https://github.com/sakuraiiiii/HermNet"

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_HermNet/rmnet.py

@@ -0,0 +1,105 @@
+import math
+
+import torch
+from torch import nn, Tensor
+import numpy as np
+
+
+_eps = 1e-3
+
+r"""Tricks: Introducing the parameter `_eps` is to avoid NaN.
+In HVNet and HTNet, a subgraph will be extracted to calculate angles. 
+And with all the nodes still be included in the subgraph, 
+each hidden state in such a subgraph will contain 0 value.
+In `painn`, the calculation w.r.t $r / \parallel r \parallel$ will be taken.
+If just alternate $r / \parallel r \parallel$ with $r / (\parallel r \parallel + _eps)$, 
+NaN will still occur in during the training.
+Considering the following example,
+$$
+(\frac{x}{r+_eps})^\prime = \frac{r+b-\frac{x^2}{r}}{(r+b)^2}
+$$
+where $r = \sqrt{x^2+y^2+z^2}$. It is obvious that NaN will occur.
+Thus the solution is change the norm $r$ as $r^\prime = \sqrt(x^2+y^2+z^2+_eps)$.
+Since $r$ is rotational invariant, $r^2$ is rotational invariant.
+Obviously, $\sqrt(r^2 + _eps)$ is rotational invariant.
+"""
+class RBF(nn.Module):
+    r"""Radial basis function.
+    A modified version of feature engineering in `DimeNet`,
+    which is used in `PAINN`.
+
+    Parameters
+    ----------
+    rc      : float
+        Cutoff radius
+    l       : int
+        Parameter in feature engineering in DimeNet
+    """
+    def __init__(self, rc: float, l: int):
+        super(RBF, self).__init__()
+        self.rc = rc
+        self.l = l
+
+    def forward(self, x: Tensor):
+        ls = torch.arange(1, self.l + 1).float().to(x.device)
+        norm = torch.sqrt((x ** 2).sum(dim=-1) + _eps).unsqueeze(-1)
+        return torch.sin(math.pi / self.rc * norm@ls.unsqueeze(0)) / norm
+
+
+class cosine_cutoff(nn.Module):
+    r"""Cutoff function in https://aip.scitation.org/doi/pdf/10.1063/1.3553717.
+
+    Parameters
+    ----------
+    rc      : float
+        Cutoff radius
+    """
+    def __init__(self, rc: float):
+        super(cosine_cutoff, self).__init__()
+        self.rc = rc
+
+    def forward(self, x: Tensor):
+        norm = torch.norm(x, dim=-1, keepdim=True) + _eps
+        return 0.5 * (torch.cos(math.pi * norm / self.rc) + 1)
+
+class ShiftedSoftplus(nn.Module):
+    r"""
+
+    Description
+    -----------
+    Applies the element-wise function:
+
+    .. math::
+        \text{SSP}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) - \log(\text{shift})
+
+    Attributes
+    ----------
+    beta : int
+        :math:`\beta` value for the mathematical formulation. Default to 1.
+    shift : int
+        :math:`\text{shift}` value for the mathematical formulation. Default to 2.
+    """
+    def __init__(self, beta=1, shift=2, threshold=20):
+        super(ShiftedSoftplus, self).__init__()
+
+        self.shift = shift
+        self.softplus = nn.Softplus(beta=beta, threshold=threshold)
+
+    def forward(self, inputs):
+        """
+
+        Description
+        -----------
+        Applies the activation function.
+
+        Parameters
+        ----------
+        inputs : float32 tensor of shape (N, *)
+            * denotes any number of additional dimensions.
+
+        Returns
+        -------
+        float32 tensor of shape (N, *)
+            Result of applying the activation function to the input.
+        """
+        return self.softplus(inputs) - np.log(float(self.shift))

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_PyG_future/__init__.py

@@ -0,0 +1,2 @@
+from .graph_norm import GraphNorm
+from .diff_group_norm import DiffGroupNorm

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_PyG_future/diff_group_norm.py

@@ -0,0 +1,109 @@
+import torch
+from torch import Tensor
+from torch.nn import Linear, BatchNorm1d
+
+
+class DiffGroupNorm(torch.nn.Module):
+    r"""The differentiable group normalization layer from the `"Towards Deeper
+    Graph Neural Networks with Differentiable Group Normalization"
+    <https://arxiv.org/abs/2006.06972>`_ paper, which normalizes node features
+    group-wise via a learnable soft cluster assignment
+
+    .. math::
+
+        \mathbf{S} = \text{softmax} (\mathbf{X} \mathbf{W})
+
+    where :math:`\mathbf{W} \in \mathbb{R}^{F \times G}` denotes a trainable
+    weight matrix mapping each node into one of :math:`G` clusters.
+    Normalization is then performed group-wise via:
+
+    .. math::
+
+        \mathbf{X}^{\prime} = \mathbf{X} + \lambda \sum_{i = 1}^G
+        \text{BatchNorm}(\mathbf{S}[:, i] \odot \mathbf{X})
+
+    Args:
+        in_channels (int): Size of each input sample :math:`F`.
+        groups (int): The number of groups :math:`G`.
+        lamda (float, optional): The balancing factor :math:`\lambda` between
+            input embeddings and normalized embeddings. (default: :obj:`0.01`)
+        eps (float, optional): A value added to the denominator for numerical
+            stability. (default: :obj:`1e-5`)
+        momentum (float, optional): The value used for the running mean and
+            running variance computation. (default: :obj:`0.1`)
+        affine (bool, optional): If set to :obj:`True`, this module has
+            learnable affine parameters :math:`\gamma` and :math:`\beta`.
+            (default: :obj:`True`)
+        track_running_stats (bool, optional): If set to :obj:`True`, this
+            module tracks the running mean and variance, and when set to
+            :obj:`False`, this module does not track such statistics and always
+            uses batch statistics in both training and eval modes.
+            (default: :obj:`True`)
+    """
+    def __init__(self, in_channels, groups, lamda=0.01, eps=1e-5, momentum=0.1,
+                 affine=True, track_running_stats=True):
+        super(DiffGroupNorm, self).__init__()
+
+        self.in_channels = in_channels
+        self.groups = groups
+        self.lamda = lamda
+
+        self.lin = Linear(in_channels, groups, bias=False)
+        self.norm = BatchNorm1d(groups * in_channels, eps, momentum, affine,
+                                track_running_stats)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.lin.reset_parameters()
+        self.norm.reset_parameters()
+
+    def forward(self, x: Tensor) -> Tensor:
+        """"""
+        F, G = self.in_channels, self.groups
+
+        s = self.lin(x).softmax(dim=-1)  # [N, G]
+        out = s.unsqueeze(-1) * x.unsqueeze(-2)  # [N, G, F]
+        out = self.norm(out.view(-1, G * F)).view(-1, G, F).sum(-2)  # [N, F]
+
+        return x + self.lamda * out
+
+    @staticmethod
+    def group_distance_ratio(x: Tensor, y: Tensor, eps: float = 1e-5) -> float:
+        r"""Measures the ratio of inter-group distance over intra-group
+        distance
+
+        .. math::
+            R_{\text{Group}} = \frac{\frac{1}{(C-1)^2} \sum_{i!=j}
+            \frac{1}{|\mathbf{X}_i||\mathbf{X}_j|} \sum_{\mathbf{x}_{iv}
+            \in \mathbf{X}_i } \sum_{\mathbf{x}_{jv^{\prime}} \in \mathbf{X}_j}
+            {\| \mathbf{x}_{iv} - \mathbf{x}_{jv^{\prime}} \|}_2 }{
+            \frac{1}{C} \sum_{i} \frac{1}{{|\mathbf{X}_i|}^2}
+            \sum_{\mathbf{x}_{iv}, \mathbf{x}_{iv^{\prime}} \in \mathbf{X}_i }
+            {\| \mathbf{x}_{iv} - \mathbf{x}_{iv^{\prime}} \|}_2 }
+
+        where :math:`\mathbf{X}_i` denotes the set of all nodes that belong to
+        class :math:`i`, and :math:`C` denotes the total number of classes in
+        :obj:`y`.
+        """
+        num_classes = int(y.max()) + 1
+
+        numerator = 0.
+        for i in range(num_classes):
+            mask = y == i
+            dist = torch.cdist(x[mask].unsqueeze(0), x[~mask].unsqueeze(0))
+            numerator += (1 / dist.numel()) * float(dist.sum())
+        numerator *= 1 / (num_classes - 1)**2
+
+        denominator = 0.
+        for i in range(num_classes):
+            mask = y == i
+            dist = torch.cdist(x[mask].unsqueeze(0), x[mask].unsqueeze(0))
+            denominator += (1 / dist.numel()) * float(dist.sum())
+        denominator *= 1 / num_classes
+
+        return numerator / (denominator + eps)
+
+    def __repr__(self):
+        return '{}({}, groups={})'.format(self.__class__.__name__,
+                                          self.in_channels, self.groups)

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_PyG_future/graph_norm.py

@@ -0,0 +1,60 @@
+from typing import Optional
+
+import torch
+from torch import Tensor
+from torch_scatter import scatter_mean
+
+from torch_geometric.nn.inits import zeros, ones
+
+
+class GraphNorm(torch.nn.Module):
+    r"""Applies graph normalization over individual graphs as described in the
+    `"GraphNorm: A Principled Approach to Accelerating Graph Neural Network
+    Training" <https://arxiv.org/abs/2009.03294>`_ paper
+
+    .. math::
+        \mathbf{x}^{\prime}_i = \frac{\mathbf{x} - \alpha \odot
+        \textrm{E}[\mathbf{x}]}
+        {\sqrt{\textrm{Var}[\mathbf{x} - \alpha \odot \textrm{E}[\mathbf{x}]]
+        + \epsilon}} \odot \gamma + \beta
+
+    where :math:`\alpha` denotes parameters that learn how much information
+    to keep in the mean.
+
+    Args:
+        in_channels (int): Size of each input sample.
+        eps (float, optional): A value added to the denominator for numerical
+            stability. (default: :obj:`1e-5`)
+    """
+    def __init__(self, in_channels: int, eps: float = 1e-5):
+        super(GraphNorm, self).__init__()
+
+        self.in_channels = in_channels
+        self.eps = eps
+
+        self.weight = torch.nn.Parameter(torch.Tensor(in_channels))
+        self.bias = torch.nn.Parameter(torch.Tensor(in_channels))
+        self.mean_scale = torch.nn.Parameter(torch.Tensor(in_channels))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        ones(self.weight)
+        zeros(self.bias)
+        ones(self.mean_scale)
+
+    def forward(self, x: Tensor, batch: Optional[Tensor] = None) -> Tensor:
+        """"""
+        if batch is None:
+            batch = x.new_zeros(x.size(0), dtype=torch.long)
+
+        batch_size = int(batch.max()) + 1
+
+        mean = scatter_mean(x, batch, dim=0, dim_size=batch_size)[batch]
+        out = x - mean * self.mean_scale
+        var = scatter_mean(out.pow(2), batch, dim=0, dim_size=batch_size)
+        std = (var + self.eps).sqrt()[batch]
+        return self.weight * out / std + self.bias
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.in_channels})'

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_PyG_future/license.txt

@@ -0,0 +1,22 @@
+The code in this folder was obtained from "https://github.com/rusty1s/pytorch_geometric", which has the following license:
+
+
+Copyright (c) 2020 Matthias Fey <matthias.fey@tu-dortmund.de>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_pymatgen/__init__.py

@@ -0,0 +1 @@
+from .lattice import find_neighbors, _one_to_three, _compute_cube_index, _three_to_one

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_pymatgen/lattice.py

@@ -0,0 +1,71 @@
+import itertools
+import numpy as np
+
+
+# The following internal methods are used in the get_points_in_sphere method.
+def _compute_cube_index(coords: np.ndarray, global_min: float, radius: float
+                        ) -> np.ndarray:
+    """
+    Compute the cube index from coordinates
+    Args:
+        coords: (nx3 array) atom coordinates
+        global_min: (float) lower boundary of coordinates
+        radius: (float) cutoff radius
+
+    Returns: (nx3 array) int indices
+
+    """
+    return np.array(np.floor((coords - global_min) / radius), dtype=int)
+
+def _three_to_one(label3d: np.ndarray, ny: int, nz: int) -> np.ndarray:
+    """
+    The reverse of _one_to_three
+    """
+    return np.array(label3d[:, 0] * ny * nz +
+                    label3d[:, 1] * nz + label3d[:, 2]).reshape((-1, 1))
+
+def _one_to_three(label1d: np.ndarray, ny: int, nz: int) -> np.ndarray:
+    """
+    Convert a 1D index array to 3D index array
+
+    Args:
+        label1d: (array) 1D index array
+        ny: (int) number of cells in y direction
+        nz: (int) number of cells in z direction
+
+    Returns: (nx3) int array of index
+
+    """
+    last = np.mod(label1d, nz)
+    second = np.mod((label1d - last) / nz, ny)
+    first = (label1d - last - second * nz) / (ny * nz)
+    return np.concatenate([first, second, last], axis=1)
+
+def find_neighbors(label: np.ndarray, nx: int, ny: int, nz: int):
+    """
+    Given a cube index, find the neighbor cube indices
+
+    Args:
+        label: (array) (n,) or (n x 3) indice array
+        nx: (int) number of cells in y direction
+        ny: (int) number of cells in y direction
+        nz: (int) number of cells in z direction
+
+    Returns: neighbor cell indices
+
+    """
+
+    array = [[-1, 0, 1]] * 3
+    neighbor_vectors = np.array(list(itertools.product(*array)),
+                                dtype=int)
+    if np.shape(label)[1] == 1:
+        label3d = _one_to_three(label, ny, nz)
+    else:
+        label3d = label
+    all_labels = label3d[:, None, :] - neighbor_vectors[None, :, :]
+    filtered_labels = []
+    # filter out out-of-bound labels i.e., label < 0
+    for labels in all_labels:
+        ind = (labels[:, 0] < nx) * (labels[:, 1] < ny) * (labels[:, 2] < nz) * np.all(labels > -1e-5, axis=1)
+        filtered_labels.append(labels[ind])
+    return filtered_labels

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_pymatgen/license.txt

@@ -0,0 +1,22 @@
+The code in this folder was obtained from "https://github.com/materialsproject/pymatgen", which has the following license:
+
+
+The MIT License (MIT)
+Copyright (c) 2011-2012 MIT & The Regents of the University of California, through Lawrence Berkeley National Laboratory
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_schnetpack/__init__.py

@@ -0,0 +1 @@
+from .acsf import GaussianBasis

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_schnetpack/acsf.py

@@ -0,0 +1,50 @@
+import torch
+from torch import nn
+
+
+def gaussian_smearing(distances, offset, widths, centered=False):
+    if not centered:
+        # compute width of Gaussian functions (using an overlap of 1 STDDEV)
+        coeff = -0.5 / torch.pow(widths, 2)
+        # Use advanced indexing to compute the individual components
+        diff = distances[..., None] - offset
+    else:
+        # if Gaussian functions are centered, use offsets to compute widths
+        coeff = -0.5 / torch.pow(offset, 2)
+        # if Gaussian functions are centered, no offset is subtracted
+        diff = distances[..., None]
+    # compute smear distance values
+    gauss = torch.exp(coeff * torch.pow(diff, 2))
+    return gauss
+
+
+class GaussianBasis(nn.Module):
+    def __init__(
+            self, start=0.0, stop=5.0, n_gaussians=50, centered=False, trainable=False
+    ):
+        super(GaussianBasis, self).__init__()
+        # compute offset and width of Gaussian functions
+        offset = torch.linspace(start, stop, n_gaussians)
+        widths = torch.FloatTensor((offset[1] - offset[0]) * torch.ones_like(offset))
+        if trainable:
+            self.width = nn.Parameter(widths)
+            self.offsets = nn.Parameter(offset)
+        else:
+            self.register_buffer("width", widths)
+            self.register_buffer("offsets", offset)
+        self.centered = centered
+
+    def forward(self, distances):
+        """Compute smeared-gaussian distance values.
+
+        Args:
+            distances (torch.Tensor): interatomic distance values of
+                (N_b x N_at x N_nbh) shape.
+
+        Returns:
+            torch.Tensor: layer output of (N_b x N_at x N_nbh x N_g) shape.
+
+        """
+        return gaussian_smearing(
+            distances, self.offsets, self.width, centered=self.centered
+        )

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_schnetpack/license.txt

@@ -0,0 +1,35 @@
+The code in this folder was obtained from "https://github.com/atomistic-machine-learning/schnetpack", which has the following license:
+
+
+COPYRIGHT
+
+Copyright (c) 2018 Kristof Schütt, Michael Gastegger, Pan Kessel, Kim Nicoli
+
+All other contributions:
+Copyright (c) 2018, the respective contributors.
+All rights reserved.
+
+Each contributor holds copyright over their respective contributions.
+The project versioning (Git) records all such contribution source information.
+
+LICENSE
+
+The MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

example/diamond/1_data_prepare/data/bands/sc/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_se3_transformer/__init__.py

@@ -0,0 +1 @@
+from .representations import SphericalHarmonics