Sync mmml checkpoints, configurations, and run metadata (part 22)

synced/mmml_new_exports/spicea_efield_models_20261007.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b8e89c2705e9d5230098a79bbbe38c158e1b13b0d384a710a688dc715865f59
+size 95739679

synced/mmml_new_exports/spicea_efield_models_20261007.zip.md5

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+8faf1255b18b2484505d4e94b6fa9205  spicea_efield_models_20261007.zip

synced/mmml_new_exports/spicea_efield_models_20261007/models/parity_rw/SPLIT.txt

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+original_split.npz in ../../splits/ (identical to this run's split.npz)

synced/mmml_new_exports/spicea_efield_models_20261007/models/parity_rw/config.json

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+{
+  "ragged_dir": "/scicore/home/meuwly/boitti0000/data/spicealpha/ragged",
+  "out_dir": "/scicore/home/meuwly/boitti0000/mmml_new/ckpts/spicea_full_v1_q10_parity_rw",
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+  "features": 64,
+  "max_degree": 2,
+  "num_iterations": 3,
+  "num_basis_functions": 32,
+  "cutoff": 5.0,
+  "gradient_checkpoint": false,
+  "charge_activation": "silu",
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+  "forces_weight": 10.0,
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+  "polar_weight": 0.1,
+  "charge_weight": 10.0,
+  "log_every": 500
+}

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synced/mmml_new_exports/spicea_efield_models_20261007/models/rw_neutral/SPLIT.txt

@@ -0,0 +1 @@
+v2_neutral_split.npz in ../../splits/ (identical to this run's split.npz)

synced/mmml_new_exports/spicea_efield_models_20261007/models/rw_neutral/config.json

@@ -0,0 +1,38 @@
+{
+  "ragged_dir": "/scicore/home/meuwly/boitti0000/data/spicealpha/ragged_v2/neutral",
+  "out_dir": "/scicore/home/meuwly/boitti0000/mmml_new/ckpts/spicea_full_rw_neutral",
+  "subsets": null,
+  "max_train_frames": 0,
+  "max_valid_frames": 20000,
+  "epochs": 49,
+  "max_steps": 0,
+  "lr": 0.001,
+  "warmup_steps": 1000,
+  "clip_norm": 10.0,
+  "ema_decay": 0.999,
+  "seed": 0,
+  "restart": "/scicore/home/meuwly/boitti0000/mmml_new/ckpts/spicea_full_v1_q10_parity_rw/analysis/stop_20261006/params-best.json",
+  "split": "/scicore/home/meuwly/boitti0000/data/spicealpha/ragged_v2/neutral_split.npz",
+  "max_molecules": 256,
+  "max_atoms": 4096,
+  "max_edges": 98304,
+  "coulomb_cutoff": null,
+  "max_coulomb_edges": 0,
+  "features": 64,
+  "max_degree": 2,
+  "num_iterations": 3,
+  "num_basis_functions": 32,
+  "cutoff": 5.0,
+  "gradient_checkpoint": false,
+  "charge_activation": "silu",
+  "pseudotensors": true,
+  "parity_correct": true,
+  "strict_pseudotensors": false,
+  "atomic_dipoles": true,
+  "energy_weight": 0.0,
+  "forces_weight": 10.0,
+  "dipole_weight": 100.0,
+  "polar_weight": 0.1,
+  "charge_weight": 10.0,
+  "log_every": 500
+}

synced/mmml_new_exports/spicea_efield_models_20261007/models/rw_neutral/history.jsonl

@@ -0,0 +1,13 @@
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synced/mmml_new_exports/spicea_efield_models_20261007/models/rw_q/SPLIT.txt

@@ -0,0 +1 @@
+v2_q_split.npz in ../../splits/ (identical to this run's split.npz)

synced/mmml_new_exports/spicea_efield_models_20261007/models/rw_q/config.json

@@ -0,0 +1,38 @@
+{
+  "ragged_dir": "/scicore/home/meuwly/boitti0000/data/spicealpha/ragged_v2/q",
+  "out_dir": "/scicore/home/meuwly/boitti0000/mmml_new/ckpts/spicea_full_rw_q",
+  "subsets": null,
+  "max_train_frames": 0,
+  "max_valid_frames": 20000,
+  "epochs": 49,
+  "max_steps": 0,
+  "lr": 0.001,
+  "warmup_steps": 1000,
+  "clip_norm": 10.0,
+  "ema_decay": 0.999,
+  "seed": 0,
+  "restart": "/scicore/home/meuwly/boitti0000/mmml_new/ckpts/spicea_full_v1_q10_parity_rw/analysis/stop_20261006/params-best.json",
+  "split": "/scicore/home/meuwly/boitti0000/data/spicealpha/ragged_v2/q_split.npz",
+  "max_molecules": 256,
+  "max_atoms": 4096,
+  "max_edges": 98304,
+  "coulomb_cutoff": null,
+  "max_coulomb_edges": 0,
+  "features": 64,
+  "max_degree": 2,
+  "num_iterations": 3,
+  "num_basis_functions": 32,
+  "cutoff": 5.0,
+  "gradient_checkpoint": false,
+  "charge_activation": "silu",
+  "pseudotensors": true,
+  "parity_correct": true,
+  "strict_pseudotensors": false,
+  "atomic_dipoles": true,
+  "energy_weight": 0.0,
+  "forces_weight": 10.0,
+  "dipole_weight": 100.0,
+  "polar_weight": 0.1,
+  "charge_weight": 10.0,
+  "log_every": 500
+}

synced/mmml_new_exports/spicea_efield_models_20261007/models/rw_q/history.jsonl

@@ -0,0 +1,9 @@
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synced/mmml_new_exports/spicea_efield_models_20261007/models/rw_q_lin/SPLIT.txt

@@ -0,0 +1 @@
+v2_q_split.npz in ../../splits/ (identical to this run's split.npz)

synced/mmml_new_exports/spicea_efield_models_20261007/models/rw_q_lin/config.json

@@ -0,0 +1,38 @@
+{
+  "ragged_dir": "/scicore/home/meuwly/boitti0000/data/spicealpha/ragged_v2/q",
+  "out_dir": "/scicore/home/meuwly/boitti0000/mmml_new/ckpts/spicea_full_rw_q_lin",
+  "subsets": null,
+  "max_train_frames": 0,
+  "max_valid_frames": 20000,
+  "epochs": 49,
+  "max_steps": 0,
+  "lr": 0.001,
+  "warmup_steps": 1000,
+  "clip_norm": 10.0,
+  "ema_decay": 0.999,
+  "seed": 0,
+  "restart": "/scicore/home/meuwly/boitti0000/mmml_new/ckpts/spicea_full_v1_q10_parity_rw/analysis/stop_20261006/params-best.json",
+  "split": "/scicore/home/meuwly/boitti0000/data/spicealpha/ragged_v2/q_split.npz",
+  "max_molecules": 256,
+  "max_atoms": 4096,
+  "max_edges": 98304,
+  "coulomb_cutoff": null,
+  "max_coulomb_edges": 0,
+  "features": 64,
+  "max_degree": 2,
+  "num_iterations": 3,
+  "num_basis_functions": 32,
+  "cutoff": 5.0,
+  "gradient_checkpoint": false,
+  "charge_activation": "linear",
+  "pseudotensors": true,
+  "parity_correct": true,
+  "strict_pseudotensors": false,
+  "atomic_dipoles": true,
+  "energy_weight": 0.0,
+  "forces_weight": 10.0,
+  "dipole_weight": 100.0,
+  "polar_weight": 0.1,
+  "charge_weight": 10.0,
+  "log_every": 500
+}

synced/mmml_new_exports/spicea_efield_models_20261007/models/rw_q_lin/history.jsonl

@@ -0,0 +1,3 @@
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synced/mmml_new_exports/spicea_efield_models_20261007/splits/original_split.npz

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synced/mmml_new_exports/spicea_efield_models_20261007/splits/ragged_v2__build_stats.json

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+ "SPICE_Water__charged": {
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+ },
+ "SPICE_amino_acid_ligand__charged": {
+  "frames": 45664,
+  "molecules": 17978
+ },
+ "SPICE_di_peptide__charged": {
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+  "molecules": 258
+ },
+ "SPICE_solvated_amino_acid__charged": {
+  "frames": 300,
+  "molecules": 6
+ },
+ "neutral": {
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+  "valid": 84476,
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+ "q": {
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+  "charged": 147791,
+  "q_nan": 0
+ }
+}

synced/mmml_new_exports/spicea_efield_models_20261007/splits/ragged_v3__neutral__alpha_filter.json

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+{
+ "frames_in": 1642132,
+ "dropped": 63,
+ "by_subset": {
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+  "dimers": 5,
+  "dipeptides": 20,
+  "monomers": 2,
+  "pubchem": 20
+ },
+ "by_shard": {
+  "DES370K_Dimers": 5,
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+  "SPICE_PubChem_3_4": 5,
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+  "SPICE_Water": 0,
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+  "SPICE_di_peptide": 20,
+  "SPICE_solvated_amino_acid": 0
+ },
+ "split": {
+  "train": 1473512,
+  "valid": 84475,
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+}

synced/mmml_new_exports/spicea_efield_models_20261007/splits/ragged_v3__neutral__frame_map_dropped.npy

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synced/mmml_new_exports/spicea_efield_models_20261007/splits/ragged_v3__neutral__frame_map_src.npy

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+ "by_subset": {
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+}

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synced/mmml_new_spice_scripts/README.md

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+# SPICE-α → efield polarizability
+
+No downloads. Point these scripts at the unzipped Zenodo 19205036 tree
+(`SPICE-alpha.zip` already extracted).
+
+Polarizability is trained on the **efield** model as an extra loss term:
+`α = dμ/dEf` evaluated at **Ef = 0** (`mmml.models.efield.model_functions`).
+Labels are converted from the release unit `e·Å²/V` to Bohr³.
+
+`train_efield_polar.sh` **forces `JAX_ENABLE_X64=0`**. SciCORE's
+`scripts/scicore_env.sh` defaults x64 on and will crash `EFieldPhysNet.init`.
+Do not source that prolog for this job. No CHARMM.
+
+```bash
+# 1. Inner HDF5 + NPZ splits (256 frames = smoke; skips the 1.3 GB dimers)
+# Published DES370K HDF5 may have an empty file-level units_map; convert
+# still reads the first molecule group, then (if that is also empty)
+# assumes SPICE-α README Å/eV units. Explicit Hartree/Bohr on that group
+# is refused unless --allow-atomic-units.
+scripts/spice_alpha/prepare_efield_dataset.sh ~/data/spicealpha ./spice_mmml 256
+# dimers later: INCLUDE_DIMERS=1 scripts/spice_alpha/prepare_efield_dataset.sh ... 0
+python scripts/spice_alpha/check_efield_npz.py \
+  ./spice_mmml/splits_des_mono/energies_forces_dipoles_{train,valid}.npz
+
+# 2. Full DES370K monomers (new tree; skip dimers). Prefer a CPU sbatch.
+SKIP_DIMERS=1 scripts/spice_alpha/prepare_efield_dataset.sh \
+  ~/data/spicealpha ~/data/spicealpha/mmml_efield_full 0
+# or: sbatch scripts/spice_alpha/prepare_efield_dataset.sbatch
+
+# 3. GPU smoke, then full. MODE=full defaults POLAR_WEIGHT=100 (1 left
+# valid polar mae frozen on the 256-frame extract).
+sbatch scripts/spice_alpha/train_efield_polar.sbatch
+# 256-frame extract: BATCH_SIZE=8 (valid n=13; B=64 → 0 valid batches)
+sbatch --time=06:00:00 --qos=rtx4090-6hours \
+  --export=ALL,MODE=full,EPOCHS=100,BATCH_SIZE=8 \
+  scripts/spice_alpha/train_efield_polar.sbatch
+# all monomers. B=16 F=64 and B=64 F=32 both died in polar-JVP XLA autotune.
+sbatch --partition=rtx4090 --qos=rtx4090-6hours --time=06:00:00 \
+  --export=ALL,MODE=big,EPOCHS=100,BATCH_SIZE=4,SPLITS=$HOME/data/spicealpha/mmml_efield_full/splits_des_mono,CKPT=$HOME/mmml/ckpts/spice_ef_polar_big \
+  scripts/spice_alpha/train_efield_polar.sbatch
+```
+
+Interactive GPU (after an allocation):
+
+```bash
+BATCH_SIZE=8 FEATURES=16 MAX_DEGREE=1 \
+  scripts/spice_alpha/train_efield_polar.sh ./spice_mmml/splits_des_mono ./ckpts/spice_ef_polar 2
+```
+
+Equivalent one-liners: `docs/spice-alpha.md`.

synced/mmml_new_spice_scripts/audit_efield_job.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+"""Audit a spice-α / efield-train Slurm job from the login node.
+
+Does not start training, download data, or touch CHARMM.
+
+    python scripts/spice_alpha/audit_efield_job.py \
+      --ckpt $HOME/mmml/ckpts/spice_ef_polar_big \
+      --log artifacts/spice_ef_polar_big/slurm-22826285.out \
+      --job 22826285
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[2]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from mmml.models.efield.audit import main
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

synced/mmml_new_spice_scripts/check_efield_npz.py

@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+"""Gate an efield-train NPZ (Ef=0, polar Bohr³). No downloads."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+
+from mmml.data.spice_alpha import check_efield_train_npz, max_atomic_number
+
+
+def _summarize(path: Path) -> None:
+    raw = np.load(path, allow_pickle=True)
+    n = int(np.asarray(raw["E"]).reshape(-1).shape[0])
+    zmax = max_atomic_number({k: raw[k] for k in raw.files})
+    polar = np.asarray(raw["polar"])
+    finite = int(np.isfinite(polar).all(axis=(-2, -1)).sum())
+    units = {}
+    if "_mmml_units" in raw.files:
+        units = json.loads(str(np.asarray(raw["_mmml_units"]).reshape(-1)[0]))
+    print(
+        f"{path}: n={n} pad={raw['R'].shape[1]} Zmax={zmax} "
+        f"polar_finite={finite}/{n} E[0]={float(np.asarray(raw['E']).reshape(-1)[0]):.4g} "
+        f"units={units.get('E')}/{units.get('polar')}"
+    )
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("npz", nargs="+", type=Path)
+    args = parser.parse_args(argv)
+    failed = 0
+    for path in args.npz:
+        problems = check_efield_train_npz(path)
+        if problems:
+            failed = 1
+            for line in problems:
+                print(line, file=sys.stderr)
+            continue
+        _summarize(path)
+    return failed
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

synced/mmml_new_spice_scripts/eval_polar_dipole.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+"""Dipole and polarizability test errors in the MACE-MDP (Fig. 2) convention.
+
+Reports RMSE of dipole components (e·Å) and of diagonal / off-diagonal
+polarizability elements, in e·Å²/V (MACE-MDP units) and Bohr³. Off-diagonal
+uses the 6 off-diagonal entries of the 3x3 tensor.
+
+Usage (GPU node):
+  python scripts/spice_alpha/eval_polar_dipole.py CKPT_DIR DATA_NPZ [--params params-best.json] [--batch-size 4]
+"""
+
+import argparse
+import functools
+import json
+from pathlib import Path
+
+import numpy as np
+
+BOHR3_PER_EA2V = 97.17  # 1 e·Å²/V = 4πε0-scaled 97.17 Bohr³
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    ap.add_argument("ckpt_dir", type=Path)
+    ap.add_argument("data_npz", type=Path)
+    ap.add_argument("--params", default="params-best.json")
+    ap.add_argument("--batch-size", type=int, default=4)
+    ap.add_argument("--save-npz", type=Path, default=None, help="write predictions and targets")
+    args = ap.parse_args()
+
+    import e3x
+    import jax
+    import jax.numpy as jnp
+
+    from mmml.models.efield.model_functions import predicted_polarizability_bohr3
+    from mmml.models.efield.training import (
+        MessagePassingModel,
+        load_ef_npz,
+        load_params,
+        prepare_batches,
+    )
+
+    cfg = json.loads((args.ckpt_dir / "config.json").read_text())
+    field_scale = cfg["model"]["field_scale"]
+    model = MessagePassingModel(**cfg["model"])
+    params = load_params(args.ckpt_dir / args.params)
+
+    data = load_ef_npz(args.data_npz)
+    B = args.batch_size
+    n_frames = int(np.asarray(data["electric_field"]).shape[0])
+    n_atoms = int(data["positions"].shape[1])
+    dst, src = e3x.ops.sparse_pairwise_indices(n_atoms)
+    offsets = jnp.arange(B, dtype=jnp.int32) * n_atoms
+    dst_flat = (jnp.asarray(dst, jnp.int32)[None, :] + offsets[:, None]).reshape(-1)
+    src_flat = (jnp.asarray(src, jnp.int32)[None, :] + offsets[:, None]).reshape(-1)
+    seg = jnp.repeat(jnp.arange(B, dtype=jnp.int32), n_atoms)
+    batches = prepare_batches(jax.random.PRNGKey(0), data, B, dst_idx_flat=dst_flat,
+                              src_idx_flat=src_flat, batch_segments=seg, shuffle=False)
+
+    # Training params carry an 'intermediates' collection; rebuild it the same way
+    # evaluate.py does so sow() tracing matches training.
+    b0 = batches[0]
+    _, state = model.apply(params, atomic_numbers=b0["atomic_numbers"], positions=b0["positions"],
+                           Ef=b0["electric_field"], dst_idx_flat=b0["dst_idx_flat"],
+                           src_idx_flat=b0["src_idx_flat"], batch_segments=b0["batch_segments"],
+                           batch_size=B, mutable=["intermediates"])
+    params = {**params, "intermediates": state["intermediates"]}
+
+    @functools.partial(jax.jit)
+    def predict(batch):
+        (_, dipole), _ = model.apply(params, atomic_numbers=batch["atomic_numbers"],
+                                     positions=batch["positions"], Ef=batch["electric_field"],
+                                     dst_idx_flat=batch["dst_idx_flat"], src_idx_flat=batch["src_idx_flat"],
+                                     batch_segments=batch["batch_segments"], batch_size=B,
+                                     mutable=["intermediates"])
+        polar = predicted_polarizability_bohr3(
+            model.apply, params,
+            batch["atomic_numbers"].reshape(B, n_atoms), batch["positions"].reshape(B, n_atoms, 3),
+            batch["dst_idx_flat"], batch["src_idx_flat"], batch["batch_segments"], B,
+            field_scale=field_scale)
+        return dipole, polar
+
+    pd, pa, td, ta = [], [], [], []
+    for batch in batches:
+        dipole, polar = predict(batch)
+        pd.append(np.asarray(dipole)); pa.append(np.asarray(polar))
+        td.append(np.asarray(batch["dipoles"])); ta.append(np.asarray(batch["polar"]))
+    pd, pa, td, ta = (np.concatenate(x) for x in (pd, pa, td, ta))
+
+    diag = np.eye(3, dtype=bool)
+    err = pa - ta
+    rmse = lambda e: float(np.sqrt(np.mean(e ** 2)))
+    res = {
+        "n_frames_evaluated": int(len(pa)), "n_frames_total": n_frames,
+        "dipole_rmse_eA": rmse(pd - td), "dipole_mae_eA": float(np.mean(np.abs(pd - td))),
+        "polar_diag_rmse_bohr3": rmse(err[:, diag]), "polar_offdiag_rmse_bohr3": rmse(err[:, ~diag]),
+        "polar_diag_mae_bohr3": float(np.mean(np.abs(err[:, diag]))),
+        "polar_offdiag_mae_bohr3": float(np.mean(np.abs(err[:, ~diag]))),
+    }
+    for k in [k for k in res if k.endswith("_bohr3")]:
+        res[k.replace("_bohr3", "_eA2V")] = res[k] / BOHR3_PER_EA2V
+    print(json.dumps(res, indent=2))
+    if args.save_npz:
+        np.savez(args.save_npz, dipole_pred=pd, dipole_ref=td, polar_pred_bohr3=pa, polar_ref_bohr3=ta)
+
+
+if __name__ == "__main__":
+    main()

synced/mmml_new_spice_scripts/filter_polar_outliers.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+"""Copy an efield splits directory, dropping frames with corrupt polarizability labels.
+
+A handful of SPICE-alpha frames carry |alpha_ij| ~ 6e11 Bohr^3 (typical max
+element ~70, 99th pct ~145). One such valid frame dominates valid_loss, so
+every run reports best_epoch=1 and params-best is the untrained epoch-1 model.
+
+Usage:
+  python scripts/spice_alpha/filter_polar_outliers.py SRC_SPLITS DST_SPLITS [--max-abs 1000]
+"""
+
+import argparse
+import json
+import shutil
+from pathlib import Path
+
+import numpy as np
+
+
+def filter_npz(src: Path, dst: Path, max_abs: float) -> dict:
+    data = np.load(src, allow_pickle=False)
+    polar = data["polar"]
+    n = polar.shape[0]
+    bad = np.abs(polar).reshape(n, -1).max(axis=1) > max_abs
+    bad |= ~np.isfinite(polar).reshape(n, -1).all(axis=1)
+    keep = ~bad
+    out = {}
+    for key in data.files:
+        arr = data[key]
+        out[key] = arr[keep] if arr.ndim > 0 and arr.shape[0] == n else arr
+    np.savez(dst, **out)
+    return {"file": src.name, "n_in": int(n), "n_dropped": int(bad.sum()),
+            "dropped_idx": np.flatnonzero(bad).tolist()}
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    ap.add_argument("src", type=Path)
+    ap.add_argument("dst", type=Path)
+    ap.add_argument("--max-abs", type=float, default=1000.0,
+                    help="drop frames with any |polar_ij| above this (Bohr^3)")
+    args = ap.parse_args()
+
+    args.dst.mkdir(parents=True, exist_ok=True)
+    report = {"src": str(args.src), "max_abs_bohr3": args.max_abs, "files": []}
+    for src in sorted(args.src.glob("*.npz")):
+        info = filter_npz(src, args.dst / src.name, args.max_abs)
+        report["files"].append(info)
+        print(f"{src.name}: kept {info['n_in'] - info['n_dropped']}/{info['n_in']}"
+              f" (dropped idx {info['dropped_idx']})")
+    for extra in args.src.glob("*.json"):
+        shutil.copy2(extra, args.dst / extra.name)
+    (args.dst / "polar_filter_report.json").write_text(json.dumps(report, indent=2))
+
+
+if __name__ == "__main__":
+    main()

synced/mmml_new_spice_scripts/prepare_efield_dataset.sbatch

@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+# CPU convert of DES370K monomers → efield NPZ (all frames, skip 1.3 GB dimers).
+# Writes a *new* tree so the 256-frame smoke splits stay put.
+#
+#   sbatch scripts/spice_alpha/prepare_efield_dataset.sbatch
+#
+# Do not source scripts/scicore_env.sh (not needed; convert is numpy/h5py).
+#
+#SBATCH --job-name=spice-ef-prep
+#SBATCH --partition=scicore
+#SBATCH --time=02:00:00
+#SBATCH --cpus-per-task=4
+#SBATCH --mem=16G
+#SBATCH --output=artifacts/spice_ef_polar/slurm-prep-%j.out
+#SBATCH --error=artifacts/spice_ef_polar/slurm-prep-%j.err
+set -euo pipefail
+
+ROOT="${MMML_REPO:-$HOME/mmml}"
+cd "$ROOT"
+mkdir -p artifacts/spice_ef_polar
+
+if [[ -z "${MMML_PYTHON:-}" && ! -x "$ROOT/.venv/bin/python" && -x "$HOME/mmml/.venv/bin/python" ]]; then
+  export MMML_PYTHON="$HOME/mmml/.venv/bin/python"
+fi
+if [[ -f "$ROOT/scripts/resolve_mmml_env.sh" ]]; then
+  # shellcheck disable=SC1091
+  source "$ROOT/scripts/resolve_mmml_env.sh"
+  mmml_resolve_env "$ROOT"
+  export PATH="$(dirname "${MMML_PYTHON}"):$PATH"
+fi
+export PYTHONPATH="$ROOT${PYTHONPATH:+:$PYTHONPATH}"
+
+SPICE="${SPICE:-$HOME/data/spicealpha}"
+OUT="${OUT:-$HOME/data/spicealpha/mmml_efield_full}"
+MAX_FRAMES="${MAX_FRAMES:-0}"
+
+export SKIP_DIMERS="${SKIP_DIMERS:-1}"
+"$ROOT/scripts/spice_alpha/prepare_efield_dataset.sh" "$SPICE" "$OUT" "$MAX_FRAMES"
+
+python "$ROOT/scripts/spice_alpha/check_efield_npz.py" \
+  "$OUT/splits_des_mono/energies_forces_dipoles_train.npz" \
+  "$OUT/splits_des_mono/energies_forces_dipoles_valid.npz"
+
+echo "PASS: full monomer NPZ at $OUT/splits_des_mono"
+echo "train: SPLITS=$OUT/splits_des_mono CKPT=$ROOT/ckpts/spice_ef_polar_full MODE=full"

synced/mmml_new_spice_scripts/prepare_efield_dataset.sh

@@ -0,0 +1,70 @@
+#!/usr/bin/env bash
+# Convert an unzipped SPICE-α tree (Zenodo 19205036) to efield-train NPZs.
+# Does not download anything. Run on the machine that holds the 12 GB zip.
+#
+# Usage:
+#   scripts/spice_alpha/prepare_efield_dataset.sh ~/data/spicealpha [out_dir] [max_frames]
+#
+# max_frames=0 means all frames. Use 256 for a smoke extract.
+# Smoke: SKIP_DIMERS=1 (default when max_frames != 0). Full dimers: INCLUDE_DIMERS=1.
+
+set -euo pipefail
+
+ROOT="${1:?unzipped SPICE-α directory (contains SPICE-alpha/SPICE-alpha.tar.gz)}"
+OUT="${2:-$ROOT/mmml_efield}"
+MAX_FRAMES="${3:-0}"
+
+ROOT="$(cd "$ROOT" && pwd)"
+mkdir -p "$OUT"
+TAR="$ROOT/SPICE-alpha/SPICE-alpha.tar.gz"
+H5_DIR="$ROOT/SPICE-alpha"
+
+if [[ ! -f "$H5_DIR/DES370K_Monomers.hdf5" ]]; then
+  if [[ ! -f "$TAR" ]]; then
+    echo "missing $TAR — unzip SPICE-alpha.zip first" >&2
+    exit 1
+  fi
+  echo "extracting DES370K monomers/dimers from tarball (members are ./DES370K_*.hdf5)"
+  EXTRACT_TAR="$TAR" EXTRACT_DIR="$H5_DIR" python -c \
+    'import os; from mmml.data.spice_alpha import extract_des370k_hdf5; extract_des370k_hdf5(os.environ["EXTRACT_TAR"], os.environ["EXTRACT_DIR"])'
+fi
+
+MAX_ARGS=()
+if [[ "$MAX_FRAMES" != "0" ]]; then
+  MAX_ARGS=(--max-frames "$MAX_FRAMES")
+fi
+
+echo "converting monomers → efield NPZ (Ef=0, polar in Bohr³, neutrals only)"
+python -m mmml.data.spice_alpha \
+  "$H5_DIR/DES370K_Monomers.hdf5" \
+  -o "$OUT/spice_des_mono.npz" \
+  --efield --polar-units bohr3 --neutral-only \
+  --split-dir "$OUT/splits_des_mono" \
+  --train-frac 0.9 --valid-frac 0.05 --test-frac 0.05 \
+  "${MAX_ARGS[@]}"
+
+# 1.3 GB dimers: skip on smoke (max_frames != 0) unless INCLUDE_DIMERS=1.
+# Set SKIP_DIMERS=1 to skip even a full convert.
+want_dimers=0
+if [[ -f "$H5_DIR/DES370K_Dimers.hdf5" ]]; then
+  if [[ "${INCLUDE_DIMERS:-0}" == "1" ]]; then
+    want_dimers=1
+  elif [[ "${SKIP_DIMERS:-0}" != "1" && "$MAX_FRAMES" == "0" ]]; then
+    want_dimers=1
+  fi
+fi
+if [[ "$want_dimers" == "1" ]]; then
+  echo "converting dimers (optional; pad from data)"
+  python -m mmml.data.spice_alpha \
+    "$H5_DIR/DES370K_Dimers.hdf5" \
+    -o "$OUT/spice_des_dimers.npz" \
+    --efield --polar-units bohr3 --neutral-only \
+    --split-dir "$OUT/splits_des_dimers" \
+    --train-frac 0.9 --valid-frac 0.05 --test-frac 0.05 \
+    "${MAX_ARGS[@]}"
+else
+  echo "skipping dimers (INCLUDE_DIMERS=1 to convert DES370K_Dimers.hdf5)"
+fi
+
+echo "wrote $OUT"
+echo "train with scripts/spice_alpha/train_efield_polar.sh $OUT/splits_des_mono"

synced/mmml_new_spice_scripts/relative_errors.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+"""Validation errors relative to the spread of the validation labels.
+
+Reads efield-train ``history.jsonl`` files and reports, per epoch,
+MAE / MAD(labels) for forces, dipoles and polarizability, plus the polar
+RMSE / sigma. 1.0 means no better than predicting the dataset mean.
+Forces are taken per component over real atoms (Z > 0), in kcal/mol/Å.
+
+Usage:
+  python scripts/spice_alpha/relative_errors.py VALID_NPZ CKPT_DIR [CKPT_DIR ...] [--all]
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+
+EV2KCAL = 23.060548
+
+
+def mad(a: np.ndarray) -> float:
+    return float(np.mean(np.abs(a - a.mean(axis=0))))
+
+
+def label_spread(valid_npz: Path) -> dict:
+    v = np.load(valid_npz)
+    real = v["Z"] > 0
+    polar = v["polar"].reshape(len(v["polar"]), -1)
+    return {
+        "F": mad(v["F"][real] * EV2KCAL),
+        "D": mad(v["D"]),
+        "P": mad(polar),
+        "P_sigma": float(np.std(polar - polar.mean(axis=0))),
+    }
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    ap.add_argument("valid_npz", type=Path)
+    ap.add_argument("ckpt_dirs", type=Path, nargs="+", help="dirs containing history.jsonl")
+    ap.add_argument("--all", action="store_true", help="print every epoch (default: first 2 + last 3)")
+    args = ap.parse_args()
+
+    s = label_spread(args.valid_npz)
+    print(f"valid label spread: MAD_F={s['F']:.2f} kcal/mol/Å  MAD_D={s['D']:.3f}  "
+          f"MAD_P={s['P']:.2f} Bohr³ (σ_P={s['P_sigma']:.2f})\n")
+    print(f"{'run':32s}{'ep':>4s}{'best':>5s}  {'F/MAD':>6s} {'D/MAD':>6s} {'P/MAD':>6s} {'P rmse/σ':>8s}")
+    for d in args.ckpt_dirs:
+        hist = d / "history.jsonl"
+        if not hist.is_file():
+            print(f"{d.name:32s}  (no history.jsonl yet)")
+            continue
+        rows = [json.loads(line) for line in hist.read_text().splitlines() if line.strip()]
+        if not args.all and len(rows) > 5:
+            rows = rows[:2] + rows[-3:]
+        for h in rows:
+            p = h["valid_polar_mae_bohr3"]
+            # valid_polar_mse is logged as mean(0.5 * diff**2), hence the factor 2 below.
+            # Corrupt polar labels (|alpha| ~ 1e11) make the polar metrics meaningless.
+            ok = p < 1e4
+            prel = f"{p / s['P']:6.3f}" if ok else "   bad"
+            prms = f"{np.sqrt(2 * h['valid_polar_mse_bohr6']) / s['P_sigma']:8.3f}" if ok else "     bad"
+            print(f"{d.name:32s}{h['epoch']:4d}{h['best_epoch']:5d}  "
+                  f"{h['valid_forces_mae_kcal'] / s['F']:6.3f} {h['valid_dipole_mae'] / s['D']:6.3f} {prel} {prms}")
+
+
+if __name__ == "__main__":
+    main()

synced/mmml_new_spice_scripts/subtract_atomic_energies.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+"""Copy an efield splits directory with per-element reference energies removed from E.
+
+SPICE-alpha E labels are absolute total energies (~1e3-1e5 in label units).
+The model's element_bias starts at zero, so it cannot reach those offsets by
+gradient descent. Fit E ~ sum_i e_{Z_i} by least squares on the TRAIN split
+and subtract it from every split; forces are unchanged. The fitted e_Z go to
+atomic_energy_refs.json so predictions can be shifted back.
+
+Usage:
+  python scripts/spice_alpha/subtract_atomic_energies.py SRC_SPLITS DST_SPLITS
+"""
+
+import argparse
+import json
+import shutil
+from pathlib import Path
+
+import numpy as np
+
+
+def composition(Z: np.ndarray, elements: np.ndarray) -> np.ndarray:
+    """(n_frames, n_elements) element counts, ignoring Z=0 padding."""
+    return (Z[:, :, None] == elements[None, None, :]).sum(axis=1).astype(np.float64)
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    ap.add_argument("src", type=Path)
+    ap.add_argument("dst", type=Path)
+    args = ap.parse_args()
+
+    train = np.load(args.src / "energies_forces_dipoles_train.npz")
+    elements = np.unique(train["Z"][train["Z"] > 0])
+    A = composition(train["Z"], elements)
+    e_ref, *_ = np.linalg.lstsq(A, train["E"].astype(np.float64), rcond=None)
+
+    args.dst.mkdir(parents=True, exist_ok=True)
+    report = {"src": str(args.src), "fit_on": "train",
+              "e_ref": {int(z): float(e) for z, e in zip(elements, e_ref)}, "splits": {}}
+    for src in sorted(args.src.glob("*.npz")):
+        data = np.load(src, allow_pickle=False)
+        out = {k: data[k] for k in data.files}
+        unknown = set(np.unique(data["Z"][data["Z"] > 0])) - set(elements.tolist())
+        if unknown:
+            raise SystemExit(f"{src.name}: elements {sorted(unknown)} absent from train fit")
+        resid = data["E"] - composition(data["Z"], elements) @ e_ref
+        out["E"] = resid.astype(data["E"].dtype)
+        np.savez(args.dst / src.name, **out)
+        report["splits"][src.name] = {"E_std_before": float(data["E"].std()),
+                                      "E_std_after": float(resid.std()),
+                                      "E_mad_after": float(np.mean(np.abs(resid - resid.mean())))}
+        print(f"{src.name}: E std {data['E'].std():.4g} -> {resid.std():.4g}")
+    for extra in args.src.glob("*.json"):
+        shutil.copy2(extra, args.dst / extra.name)
+    (args.dst / "atomic_energy_refs.json").write_text(json.dumps(report, indent=2))
+
+
+if __name__ == "__main__":
+    main()

synced/mmml_new_spice_scripts/summarize_efield_lc.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python3
+"""Print a polar learning-curve table from efield ckpt dirs (history.jsonl)."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+from mmml.data.efield_lc import format_lc_table, summarize_lc_runs
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("ckpt_dir", nargs="+", type=Path)
+    args = p.parse_args(argv)
+    print(format_lc_table(summarize_lc_runs(args.ckpt_dir)), end="")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

synced/mmml_new_spice_scripts/train_efield_polar.sbatch

@@ -0,0 +1,127 @@
+#!/usr/bin/env bash
+# GPU efield-train + polar loss (SPICE-α, Ef=0).
+#
+# Smoke (default): 2 epochs, small model — prove polar MAE prints and params write.
+# Full on the 256-frame extract (valid n=13): BATCH_SIZE must be <= 13
+#   sbatch --time=06:00:00 --qos=rtx4090-6hours \
+#     --export=ALL,MODE=full,EPOCHS=100,BATCH_SIZE=8 \
+#     scripts/spice_alpha/train_efield_polar.sbatch
+# Full DES (pad=22, polar jacrev): B=64 and B=16 both died in XLA autotune.
+#   --export=ALL,MODE=big,BATCH_SIZE=4,SPLITS=...,CKPT=...
+# Restart a TIMEOUT run (new CKPT dir; do not overlap a RUNNING job):
+#   --export=ALL,MODE=big,BATCH_SIZE=4,RESTART=.../params-best-<uuid>.json,CKPT=..._r2
+#
+# Do not source scripts/scicore_env.sh for this job: it sets JAX_ENABLE_X64=1
+# and EFieldPhysNet.init fails (e3x Embed float32 vs MessagePass float64).
+# The train wrapper forces JAX_ENABLE_X64=0.
+#
+#SBATCH --job-name=spice-ef-polar
+#SBATCH --partition=rtx4090
+#SBATCH --qos=rtx4090-30min
+#SBATCH --time=00:30:00
+#SBATCH --gres=gpu:1
+#SBATCH --cpus-per-task=4
+#SBATCH --mem=32G
+#SBATCH --output=artifacts/spice_ef_polar/slurm-%j.out
+#SBATCH --error=artifacts/spice_ef_polar/slurm-%j.err
+set -euo pipefail
+
+ROOT="${MMML_REPO:-$HOME/mmml}"
+cd "$ROOT"
+mkdir -p artifacts/spice_ef_polar
+
+if [[ -z "${MMML_PYTHON:-}" && ! -x "$ROOT/.venv/bin/python" && -x "$HOME/mmml/.venv/bin/python" ]]; then
+  export MMML_PYTHON="$HOME/mmml/.venv/bin/python"
+fi
+if [[ -f "$ROOT/scripts/resolve_mmml_env.sh" ]]; then
+  # shellcheck disable=SC1091
+  source "$ROOT/scripts/resolve_mmml_env.sh"
+  mmml_resolve_env "$ROOT"
+  export PATH="$(dirname "${MMML_PYTHON}"):$PATH"
+fi
+export PYTHONPATH="$ROOT${PYTHONPATH:+:$PYTHONPATH}"
+export JAX_ENABLE_X64=0
+export PYTHONUNBUFFERED=1
+export XLA_PYTHON_CLIENT_PREALLOCATE=false
+export XLA_PYTHON_CLIENT_MEM_FRACTION="${XLA_PYTHON_CLIENT_MEM_FRACTION:-0.85}"
+export XLA_FLAGS="${XLA_FLAGS:---xla_gpu_autotune_level=0}"
+
+SPLITS="${SPLITS:-$HOME/data/spicealpha/mmml_efield/splits_des_mono}"
+CKPT="${CKPT:-$ROOT/ckpts/spice_ef_polar}"
+MODE="${MODE:-smoke}"
+
+if [[ "$MODE" == "big" ]]; then
+  # Capacity bump that still fits polar dμ/dEf on a 4090. Cutoff stays 10 Å:
+  # DES monomers are small isolated clusters; 10 Å is already all-pairs for
+  # most of them. max_degree=3 roughly doubles irreps × jacrev memory.
+  EPOCHS="${EPOCHS:-100}"
+  export BATCH_SIZE="${BATCH_SIZE:-4}"
+  export FEATURES="${FEATURES:-64}"
+  export MAX_DEGREE="${MAX_DEGREE:-2}"
+  export NUM_ITERATIONS="${NUM_ITERATIONS:-3}"
+  export NUM_BASIS_FUNCTIONS="${NUM_BASIS_FUNCTIONS:-32}"
+  export CUTOFF="${CUTOFF:-10.0}"
+  export POLAR_WEIGHT="${POLAR_WEIGHT:-100}"
+  export GRADIENT_CHECKPOINT="${GRADIENT_CHECKPOINT:-1}"
+elif [[ "$MODE" == "full" ]]; then
+  EPOCHS="${EPOCHS:-100}"
+  export BATCH_SIZE="${BATCH_SIZE:-8}"
+  export FEATURES="${FEATURES:-32}"
+  export MAX_DEGREE="${MAX_DEGREE:-2}"
+  export NUM_ITERATIONS="${NUM_ITERATIONS:-2}"
+  export NUM_BASIS_FUNCTIONS="${NUM_BASIS_FUNCTIONS:-10}"
+  export CUTOFF="${CUTOFF:-10.0}"
+  # 256-frame run at polar_weight=1 left valid polar mae frozen (~40 Bohr³);
+  # total |E| ~1e5 eV dominates. Full-monomer default raises polar loss.
+  export POLAR_WEIGHT="${POLAR_WEIGHT:-100}"
+else
+  EPOCHS="${EPOCHS:-2}"
+  export BATCH_SIZE="${BATCH_SIZE:-8}"
+  export FEATURES="${FEATURES:-16}"
+  export MAX_DEGREE="${MAX_DEGREE:-1}"
+fi
+
+# Some sciCORE nodes (seen on sgd01) expose every GPU to every job while still
+# setting CUDA_VISIBLE_DEVICES=0, so concurrent jobs pile onto one card. If the
+# visible device 0 is already busy, move to the least-used visible GPU.
+if command -v nvidia-smi >/dev/null 2>&1; then
+  n_visible="$(nvidia-smi -L | wc -l)"
+  if [[ "$n_visible" -gt 1 ]]; then
+    # Pick under a per-node lock and record the claim, so jobs that start at
+    # the same moment (before either has allocated memory) cannot pick the same card.
+    claim_dir="$HOME/.cache/gpu_guard/$(hostname)"; mkdir -p "$claim_dir"
+    exec 9>"$claim_dir/.lock"; flock 9
+    for f in "$claim_dir"/*.claim; do
+      [[ -e "$f" ]] || continue
+      squeue -h -j "$(basename "$f" .claim)" -t R 2>/dev/null | grep -q . || rm -f "$f"
+    done
+    claimed="$(cat "$claim_dir"/*.claim 2>/dev/null | sort -u || true)"
+    pick=""
+    while IFS=, read -r idx uuid used; do
+      uuid="${uuid// /}"; used="${used// /}"
+      grep -qx "$uuid" <<<"$claimed" && continue
+      [[ "$used" -gt 1000 ]] && continue
+      pick="$idx"; pick_uuid="$uuid"; break
+    done < <(nvidia-smi --query-gpu=index,uuid,memory.used --format=csv,noheader,nounits)
+    if [[ -n "$pick" ]]; then
+      [[ "$pick" != "${CUDA_VISIBLE_DEVICES:-0}" ]] && echo "GPU guard: using GPU $pick ($pick_uuid; ${n_visible} visible)" >&2
+      export CUDA_VISIBLE_DEVICES="$pick"
+      echo "$pick_uuid" > "$claim_dir/${SLURM_JOB_ID}.claim"
+    else
+      echo "GPU guard: no unclaimed idle GPU among ${n_visible}; keeping ${CUDA_VISIBLE_DEVICES:-0}" >&2
+    fi
+    flock -u 9; exec 9>&-
+  fi
+  mkdir -p "$CKPT"
+  nvidia-smi -i "${CUDA_VISIBLE_DEVICES:-0}" --query-gpu=timestamp,uuid,memory.used,utilization.gpu \
+    --format=csv,noheader -l 30 > "$CKPT/gpu_usage.csv" 2>/dev/null &
+  GPU_LOGGER_PID=$!
+  trap 'kill "$GPU_LOGGER_PID" 2>/dev/null || true; rm -f "$HOME/.cache/gpu_guard/$(hostname)/${SLURM_JOB_ID}.claim"' EXIT
+fi
+
+echo "efield-train mode=$MODE epochs=$EPOCHS B=$BATCH_SIZE F=$FEATURES L=$MAX_DEGREE T=${NUM_ITERATIONS:-2} nbasis=${NUM_BASIS_FUNCTIONS:-10} rc=${CUTOFF:-10} polar_w=${POLAR_WEIGHT:-1}"
+
+"$ROOT/scripts/spice_alpha/train_efield_polar.sh" "$SPLITS" "$CKPT" "$EPOCHS"
+
+echo "PASS: efield polar train finished mode=$MODE ckpt=$CKPT"
+ls -1 "$CKPT"/params-*.json | tail -n 5

synced/mmml_new_spice_scripts/train_efield_polar.sh

@@ -0,0 +1,84 @@
+#!/usr/bin/env bash
+# Zero-field efield-train with polarizability loss (dμ/dEf at Ef=0).
+#
+# Usage:
+#   scripts/spice_alpha/train_efield_polar.sh splits_des_mono [ckpt_dir] [epochs]
+
+set -euo pipefail
+
+# Do not put a closing brace inside `${1:?...}` (bash ends the expansion
+# there). A train/valid brace list made SPLITS `.../splits_des_mono.npz}`.
+SPLITS="${1:?splits directory with train and valid NPZs}"
+CKPT="${2:-./ckpts/spice_des_mono_efield_polar}"
+EPOCHS="${3:-100}"
+if [[ ! -d "$SPLITS" ]]; then
+  echo "SPLITS is not a directory: $SPLITS" >&2
+  exit 1
+fi
+
+# SciCORE prolog defaults JAX_ENABLE_X64=1. e3x Embed stays float32 while
+# MessagePass promotes, and EFieldPhysNet.init raises in e3x.nn.add.
+# load_ef_npz is float32; keep the process on float32 for efield-train.
+export JAX_ENABLE_X64=0
+export PYTHONUNBUFFERED="${PYTHONUNBUFFERED:-1}"
+export XLA_PYTHON_CLIENT_PREALLOCATE="${XLA_PYTHON_CLIENT_PREALLOCATE:-false}"
+export XLA_PYTHON_CLIENT_MEM_FRACTION="${XLA_PYTHON_CLIENT_MEM_FRACTION:-0.85}"
+# Polar jacrev autotune OOMs (login12: "All configs failed during profiling"
+# on MessagePass transpose). Level 0 skips that profiler.
+export XLA_FLAGS="${XLA_FLAGS:---xla_gpu_autotune_level=0}"
+
+BATCH_SIZE="${BATCH_SIZE:-64}"
+FEATURES="${FEATURES:-32}"
+MAX_DEGREE="${MAX_DEGREE:-2}"
+NUM_ITERATIONS="${NUM_ITERATIONS:-2}"
+NUM_BASIS_FUNCTIONS="${NUM_BASIS_FUNCTIONS:-10}"
+CUTOFF="${CUTOFF:-10.0}"
+ENERGY_WEIGHT="${ENERGY_WEIGHT:-1.0}"
+FORCES_WEIGHT="${FORCES_WEIGHT:-100.0}"
+DIPOLE_WEIGHT="${DIPOLE_WEIGHT:-0.1}"
+CHARGE_WEIGHT="${CHARGE_WEIGHT:-1000.0}"
+POLAR_WEIGHT="${POLAR_WEIGHT:-1.0}"
+
+HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+if [[ -f "$HERE/check_efield_npz.py" ]]; then
+  python "$HERE/check_efield_npz.py" \
+    "$SPLITS/energies_forces_dipoles_train.npz" \
+    "$SPLITS/energies_forces_dipoles_valid.npz"
+fi
+
+EXTRA=()
+if [[ "${GRADIENT_CHECKPOINT:-0}" != "0" ]]; then
+  EXTRA+=(--gradient-checkpoint)
+fi
+if [[ -n "${SAVE_FORMAT:-}" ]]; then
+  EXTRA+=(--save-format "$SAVE_FORMAT")
+fi
+if [[ -n "${SAVE_EVERY:-}" ]]; then
+  EXTRA+=(--save-every "$SAVE_EVERY")
+fi
+if [[ -n "${LEARNING_RATE:-}" ]]; then
+  EXTRA+=(--learning_rate "$LEARNING_RATE")
+fi
+if [[ -n "${RESTART:-}" ]]; then
+  EXTRA+=(--restart "$RESTART")
+fi
+
+mmml efield-train \
+  --train-npz "$SPLITS/energies_forces_dipoles_train.npz" \
+  --valid-npz "$SPLITS/energies_forces_dipoles_valid.npz" \
+  --output-dir "$CKPT" \
+  --energy_weight "$ENERGY_WEIGHT" \
+  --forces_weight "$FORCES_WEIGHT" \
+  --dipole_weight "$DIPOLE_WEIGHT" \
+  --charge_weight "$CHARGE_WEIGHT" \
+  --polar_weight "$POLAR_WEIGHT" \
+  --polar-at-zero-field \
+  --field_scale 0.001 \
+  --num_epochs "$EPOCHS" \
+  --batch_size "$BATCH_SIZE" \
+  --features "$FEATURES" \
+  --max_degree "$MAX_DEGREE" \
+  --num_iterations "$NUM_ITERATIONS" \
+  --num_basis_functions "$NUM_BASIS_FUNCTIONS" \
+  --cutoff "$CUTOFF" \
+  "${EXTRA[@]}"