The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
target: int64
successful: int64
completion_job_timed_out: bool
results: list<item: struct<omol_index: int64, candidate_kind: string, status: string>>
child 0, item: struct<omol_index: int64, candidate_kind: string, status: string>
child 0, omol_index: int64
child 1, candidate_kind: string
child 2, status: string
records_sha256: string
n_validated: int64
cluster_counts: struct<fir: int64, nibi: int64, narval: int64, rorqual: int64>
child 0, fir: int64
child 1, nibi: int64
child 2, narval: int64
child 3, rorqual: int64
previously_published: int64
completed_unique: int64
skipped_pilot_indices: list<item: int64>
child 0, item: int64
created_at: timestamp[s]
hessian_dtype: string
original_geometry_checked: bool
to
{'created_at': Value('timestamp[s]'), 'n_validated': Value('int64'), 'completed_unique': Value('int64'), 'previously_published': Value('int64'), 'cluster_counts': {'fir': Value('int64'), 'nibi': Value('int64'), 'narval': Value('int64'), 'rorqual': Value('int64')}, 'original_geometry_checked': Value('bool'), 'hessian_dtype': Value('string'), 'records_sha256': Value('string'), 'skipped_pilot_indices': List(Value('int64'))}
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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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
target: int64
successful: int64
completion_job_timed_out: bool
results: list<item: struct<omol_index: int64, candidate_kind: string, status: string>>
child 0, item: struct<omol_index: int64, candidate_kind: string, status: string>
child 0, omol_index: int64
child 1, candidate_kind: string
child 2, status: string
records_sha256: string
n_validated: int64
cluster_counts: struct<fir: int64, nibi: int64, narval: int64, rorqual: int64>
child 0, fir: int64
child 1, nibi: int64
child 2, narval: int64
child 3, rorqual: int64
previously_published: int64
completed_unique: int64
skipped_pilot_indices: list<item: int64>
child 0, item: int64
created_at: timestamp[s]
hessian_dtype: string
original_geometry_checked: bool
to
{'created_at': Value('timestamp[s]'), 'n_validated': Value('int64'), 'completed_unique': Value('int64'), 'previously_published': Value('int64'), 'cluster_counts': {'fir': Value('int64'), 'nibi': Value('int64'), 'narval': Value('int64'), 'rorqual': Value('int64')}, 'original_geometry_checked': Value('bool'), 'hessian_dtype': Value('string'), 'records_sha256': Value('string'), 'skipped_pilot_indices': List(Value('int64'))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
HIP-UMA-OMol25
UMA-S-1.2 (uma-s-1p2) autograd Hessian predictions on a subset of OMol-1 train geometries, using the omol task.
Current snapshot (2026-10-05): 24,418 complete shards, 480.8 GB, 8,818,645 samples. The goal is around 10M samples (41911 shards planned).
Shard files are grouped to stay under Hugging Face's 10,000-files-per-directory limit:
shards/00/shard_00000.h5 … shards/41/shard_41910.h5
Contents
Each HDF5 file contains molecular configurations with:
atomic_numbers,coords, andnatomscharge,spin,spin_multiplicity, andunrestrictedenergy,forces, and densehessian_flatatom_ptrandhessian_ptrfor reconstructing variable-size arrays- OMol matching fields:
omol_index,source,data_id,sid,reference_source, andcomposition - optional OMol reference labels:
dft_energy,dft_forces, andhas_dft_labels
The teacher labels are float32:
- energy: eV
- forces: eV/Angstrom
- Hessian: eV/Angstrom^2
The Hessian is dense, symmetrized, and has shape (3N, 3N).
Downloading and selecting OMol-1 geometries
The samples are selected from the OMol-1 train release 260123.
See the official OMol25 page.
Download the complete 592 GB ASE-LMDB archive:
wget -c \
https://dl.fbaipublicfiles.com/opencatalystproject/data/omol/260123/train.tar.gz
tar -xf train.tar.gz
The 10M indices that we are using are saved in the manifest.
Download:
uvx --from huggingface_hub hf download \
andreasburger/HIP-UMA-OMol25 \
manifests/sample_10m.npz \
manifests/sample_10m.meta.json \
--repo-type dataset \
--local-dir .
The indices from the manifest are designed to be used with the extracted ASE-LMDB records. Use a contiguous index range to process only one part:
import numpy as np
from fairchem.core.datasets import AseDBDataset
manifest = np.load("manifests/sample_10m.npz")
selected = manifest["indices"]
dataset = AseDBDataset({"src": "/path/to/extracted/train"})
start, stop = 0, 100_000
for omol_index in selected[start:stop]:
atoms = dataset.get_atoms(int(omol_index))
# Process atoms here.
Reproducing UMA inputs
Use task_name=omol, UMA-S-1.2, FP32, and a 120 Angstrom molecular cell.
The OMol25 spin field is the spin multiplicity. Charge and spin are
required inputs and must be passed through the equivalent of
r_data_keys=["spin", "charge"].
Provenance
- Parent dataset: OMol-1 train, release
260123 - Sampling: fixed-seed uniform sample, seed
20260729 - Model:
uma-s-1p2 - Hessian mode: loop and batched autograd; see each shard attribute
- Precision: float32
omol_index is the index into the parent OMol-1 train ASE-LMDB.
Format
We use HDF5. It preserves random access and variable-size dense Hessians without padding. For training, FairChem graph/training caches should be generated locally from these files because their representation depends on FairChem version and training configuration.
The verified October 5 snapshot contains only complete shards. Older partial
checkpoints remain in the repository; exclude files ending in .partial.h5.
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