Datasets:

MurrellLab
/

GenBench

This directory contains the with-hydrogen QM9 molecular geometries and the exact train/validation/test split used by the QM9 experiments in the TD-jumps paper, Trans-Dimensional Generative Modeling via Jump Diffusion Models.

The split was reconstructed from the released jump-diffusion code at commit b2e31007d2c5792dddc1bf03f2e92bf889f42fd8. The stored split arrays were verified byte-for-byte against an independent reconstruction of the TD-jumps row arrays.

Origin and filtering

The molecular data come from the official GDB9/QM9 XYZ archive:

Archive: https://ndownloader.figshare.com/files/3195389
Archive SHA-256: 3a63848ac80691bdb8d41834b575afad345b9300d7a2db0c38adb7f6eaa8360c
Official uncharacterized-molecule list: https://ndownloader.figshare.com/files/3195404
Exclusion-list SHA-256: 3aa5115d540b356de94791d4a74c3bf1ed91c469ecf52a4f5d7cc0506fe02e24

The original archive contains 133,885 molecules. The same 3,054 uncharacterized molecules excluded by the released TD-jumps loader were removed, leaving 130,831 molecules. qm9.npz stores those retained molecules in their original archive order after filtering; the split files contain the TD-jumps permutation as row indices into that packed file.

Exact TD-jumps split

The released TD-jumps code applies NumPy's legacy RandomState/MT19937 permutation with seed 0 to the 130,831 retained molecules, then divides that permutation as follows:

File	Molecules	Permutation positions
`train_rows.npy`	100,000	0--99,999
`valid_rows.npy`	17,748	100,000--117,747
`test_rows.npy`	13,083	117,748--130,830

Each split file is a one-dimensional int64 NumPy array of zero-based rows in qm9.npz. The three arrays are disjoint, contain every row exactly once, and retain the original seed-0 permutation order used by TD-jumps. They are not GDB9 molecule IDs; the corresponding one-based GDB9 ID for a packed row is available in qm9.npz as source_ids[row].

For example:

import numpy as np

with np.load("qm9.npz", allow_pickle=False) as qm9:
    offsets = qm9["molecule_offsets"]
    coordinates = qm9["coords"]
    atomic_numbers = qm9["atomic_numbers"]
    train_rows = np.load("train_rows.npy", allow_pickle=False)

    row = int(train_rows[0])
    left, right = offsets[row : row + 2]
    first_training_molecule = {
        "coords": coordinates[left:right],
        "atomic_numbers": atomic_numbers[left:right],
    }

Packed data schema

qm9.npz is a compressed NumPy archive containing:

Array	Shape	Dtype	Meaning
`coords`	`(2_359_210, 3)`	`float32`	Concatenated XYZ coordinates in angstroms
`atomic_numbers`	`(2_359_210,)`	`int16`	Concatenated atomic numbers
`molecule_offsets`	`(130_832,)`	`int64`	Half-open offsets into the atom arrays
`source_ids`	`(130_831,)`	`int64`	One-based IDs in the official GDB9 archive
`vocabulary_atomic_numbers`	`(5,)`	`int16`	Model vocabulary order `[6, 1, 7, 8, 9]`

The atom order inside each molecule is the order in the official XYZ file; the packed dataset does not reorder atoms. In 130,372 molecules all hydrogens follow all heavy atoms, 417 molecules have hydrogens intermingled with heavy atoms, and 42 molecules contain no hydrogen. The released TD-jumps training configuration subsequently randomizes the real-atom ordering on each dataset fetch (shuffle_node_ordering=true), but that augmentation is not baked into these files.

File checksums

SHA-256 checksums of the files in this directory:

File	SHA-256
`qm9.npz`	`ddba2129feb7f1676ae0137820d08e489555b8d928c2b943f80be6673dc2ef26`
`train_rows.npy`	`aae8ded4c106664ca06029f697bd6f4b9d2ebd12f1ab8b0506950aa79d3613f1`
`valid_rows.npy`	`ac3a8de760f21a278a64375c2d1aa01468ba73a13cf42613a6511882a41871e7`
`test_rows.npy`	`94bd03e0e51002a954030efe7610e8b81a44dbd51f57d1a9a96d50d0f21c4990`

The reconstruction verified all 130,831 retained molecules against the MurrellLab QM9 serialization: element arrays matched exactly and coordinates were bitwise identical after conversion to float32.