2_training/README.md

2_training — ML model training for diamond

Two models are trained: a Hamiltonian model (DeepH-E3) and a force-field model (NequIP). All input data comes from ../1_data_prepare/data/.

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Hamiltonian model (hamiltonian/)

Data

Training data is the AO-basis Hamiltonian H(R) computed by HPRO for each displaced supercell:

1_data_prepare/data/disp-{01..50}/reconstruction/aohamiltonian/hamiltonians.h5

train_ham.py builds symlinks from those 50 directories into hamiltonian/dataset/00/ … 49/ before the graph is constructed. This is necessary to avoid accidentally picking up the bands/*/reconstruction/aohamiltonian/ structures that also live under 1_data_prepare/data/.

The DeepH-E3 graph is then built from hamiltonian/dataset/ and saved to hamiltonian/graph/. On subsequent runs the cached graph is reused automatically.

Training

cd hamiltonian/
conda activate deeph
python train_ham.py                   # uses 1_data_prepare/params.json

train_ham.py will:

1. Build dataset/ symlinks (skipped if graph already cached).
2. Write train.ini and _launcher.py from params.json.
3. Launch training and monitor val-loss against reference milestones every 300 epochs.
4. Terminate early if val-loss is more than 5× above the reference.

Key hyperparameters (from params.json → hamiltonian):

param	value
train / val / test	30 / 10 / 10
num_epoch	3000
cutoff_radius	7.4 Å
lmax	4
irreps_mid	64x0e+32x1o+16x2e+8x3o+8x4e

Reference best: epoch 2017, val_loss = 1.98 × 10⁻⁶.

The trained model is saved under hamiltonian/results/<timestamp>_diamond_e3/best_model.pkl.

Inference and band comparison

cd hamiltonian/
conda activate deeph
python compare_bands.py               # uses 1_data_prepare/params.json

compare_bands.py will:

1. Build inference datasets for the pristine unit cell (UC) and 2×2×2 supercell (SC) from 1_data_prepare/data/bands/{uc,sc}/reconstruction/aohamiltonian/.
2. Write infer_uc/eval.ini and infer_sc/eval.ini pointing to the latest model in results/.
3. Run DeepH-E3 inference; predicted Hamiltonians are written to infer_{uc,sc}/output/00/hamiltonians_pred.h5.
4. Diagonalize QE, HPRO, and ML Hamiltonians along the G→X→W→K→G→L k-path.
5. Save band_compare_uc_ml.png and band_compare_sc_ml.png.

Predictions are cached: if hamiltonians_pred.h5 already exists in infer_{uc,sc}/output/00/, inference is skipped and only the band plot is regenerated.

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Force-field model (forces/)

Data

Forces are read from the QE SCF outputs for each displaced configuration:

1_data_prepare/data/disp-{01..50}/scf/pw.out

The combined dataset is written to forces/dataset.xyz (ASE extended XYZ, 50 frames × ~16 atoms).

Training

cd forces/
conda activate deeph
python train_force.py                 # uses 1_data_prepare/params.json
python train_force.py --skip-training # reuse existing checkpoint, replot phonons

train_force.py will:

1. Build forces/dataset.xyz from QE outputs (re-runs SCF with tprnfor=.true. for any displacement that lacks force output).
2. Run DFPT (ph.x) on the pristine UC at q=Γ to get the reference phonon frequencies.
3. Train NequIP (nequip-train forces/train.yaml) in the deeph conda env.
4. Compile the model to forces/diamond_ase.nequip.pth.
5. Run phonopy with the NequIP calculator to get ML Γ-point frequencies.
6. Save forces/phonon_comparison.png and print DFPT vs ML comparison.

The compiled model (diamond_ase.nequip.pth) is used downstream by the EPC pipeline.