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Datasets:
divelab
/
OrbFlow-data

Size:
100K<n<1M
Tags:
chemistry
electron-density
qm9
molecular-dynamics
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
train: list<item: int64>
  child 0, item: int64
validation: list<item: int64>
  child 0, item: int64
test: list<item: int64>
  child 0, item: int64
hydra_overrides: struct<model.flow_prior_mu: double, model.flow_prior_sigma: double>
  child 0, model.flow_prior_mu: double
  child 1, model.flow_prior_sigma: double
n_graphs: int64
space: string
residual_gt_minus_sad: null
gt: struct<count: int64, mean: double, std: double, rms: double, abs_mean: double, suggested_flow_prior_ (... 47 chars omitted)
  child 0, count: int64
  child 1, mean: double
  child 2, std: double
  child 3, rms: double
  child 4, abs_mean: double
  child 5, suggested_flow_prior_mu: double
  child 6, suggested_flow_prior_sigma: double
prior_stats_tag: string
note_residual: string
beta: double
n_coeff_elements: int64
to
{'n_graphs': Value('int64'), 'n_coeff_elements': Value('int64'), 'space': Value('string'), 'gt': {'count': Value('int64'), 'mean': Value('float64'), 'std': Value('float64'), 'rms': Value('float64'), 'abs_mean': Value('float64'), 'suggested_flow_prior_mu': Value('float64'), 'suggested_flow_prior_sigma': Value('float64')}, 'hydra_overrides': {'model.flow_prior_mu': Value('float64'), 'model.flow_prior_sigma': Value('float64')}, 'residual_gt_minus_sad': Value('null'), 'note_residual': Value('string'), 'prior_stats_tag': Value('string'), 'beta': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              train: list<item: int64>
                child 0, item: int64
              validation: list<item: int64>
                child 0, item: int64
              test: list<item: int64>
                child 0, item: int64
              hydra_overrides: struct<model.flow_prior_mu: double, model.flow_prior_sigma: double>
                child 0, model.flow_prior_mu: double
                child 1, model.flow_prior_sigma: double
              n_graphs: int64
              space: string
              residual_gt_minus_sad: null
              gt: struct<count: int64, mean: double, std: double, rms: double, abs_mean: double, suggested_flow_prior_ (... 47 chars omitted)
                child 0, count: int64
                child 1, mean: double
                child 2, std: double
                child 3, rms: double
                child 4, abs_mean: double
                child 5, suggested_flow_prior_mu: double
                child 6, suggested_flow_prior_sigma: double
              prior_stats_tag: string
              note_residual: string
              beta: double
              n_coeff_elements: int64
              to
              {'n_graphs': Value('int64'), 'n_coeff_elements': Value('int64'), 'space': Value('string'), 'gt': {'count': Value('int64'), 'mean': Value('float64'), 'std': Value('float64'), 'rms': Value('float64'), 'abs_mean': Value('float64'), 'suggested_flow_prior_mu': Value('float64'), 'suggested_flow_prior_sigma': Value('float64')}, 'hydra_overrides': {'model.flow_prior_mu': Value('float64'), 'model.flow_prior_sigma': Value('float64')}, 'residual_gt_minus_sad': Value('null'), 'note_residual': Value('string'), 'prior_stats_tag': Value('string'), 'beta': Value('float64')}
              because column names don't match

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OrbFlow data

Preprocessed training and evaluation data for OrbFlow (equivariant flow matching for atomic-orbital coefficients and electron density).

Contents

The layout matches the DATAPATH folder expected by the OrbFlow scripts.

Path Size Description
datasplits.json 1 MB QM9 train / validation / test split
lmdb/ 1.4 TB QM9 LMDB: geometries and density probes, 134 shards (+ metadata.json)
lmdb_gt_ridge/ 12 GB QM9 target orbital coefficients (β = 1.3, ridge 1e-6), one shard per lmdb/ shard
MD/scdp_lmdb/<mol>/ 24 GB MD LMDBs for benzene, ethane, ethanol, malonaldehyde, phenol, resorcinol (each with its own datasplits.json)
MD/scdp_lmdb_gt_ridge_beta1.3/<mol>/ 0.6 GB MD target orbital coefficients (β = 1.3, ridge 1e-6)
MD/flow_prior_ridge1e6_beta1.3/<mol>/ < 1 MB Per-molecule flow-prior µ/σ used by the MD training recipe

Download

Everything (≈ 1.46 TB):

hf download divelab/OrbFlow-data --repo-type dataset --local-dir "$DATAPATH"

Only part of it, e.g. the MD data (≈ 25 GB) or the QM9 target coefficients:

hf download divelab/OrbFlow-data --repo-type dataset --local-dir "$DATAPATH" --include "MD/*"
hf download divelab/OrbFlow-data --repo-type dataset --local-dir "$DATAPATH" --include "lmdb_gt_ridge/*" --include datasplits.json

How the data was produced

  • QM9 LMDB — built from the DTU QM9 VASP charge densities with the SCDP preprocessing (bond-midpoint virtual nodes, atom cutoff 6 Å): vision/preprocess_qm9_lmdb.slurm in the OrbFlow code. It can be rebuilt from the DTU tarballs instead of downloaded.
  • Target coefficients — per-molecule ridge fit of the DFT density on the def2-QZVPPD-derived Gaussian basis (β = 1.3, ridge 1e-6, full probe grid, float64): vision/prepare_flow_gt_coeffs_ridge1e6_gpu.slurm (QM9) and vision/prepare_md_gt_coeffs_ridge1e6_beta13_gpu.slurm (MD).
  • MD prior — mean / std of the train-split target coefficients: vision/compute_md_flow_prior_stats_beta13.slurm.
  • MD densities — TODO: source and citation.

Alignment: lmdb_gt_ridge/ is matched to lmdb/ by shard file name and entry order, not by molecule ID. Use it with the lmdb/ from this repository, or with an LMDB rebuilt by the unmodified script from all 134 original DTU tarballs. A mismatched LMDB makes training stop with a clear error.

Citation

TODO: citation.
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Total file size:
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Models trained or fine-tuned on divelab/OrbFlow-data