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REVIEW 3 major objections 6 minor 117 references

OpenQDC: Open Quantum Data Commons

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read OpenQDC collects 37 quantum-mechanical datasets, standardizes them into one Python-accessible format for training machine-learning interatomic potentials, and reports baseline results showing that established architectures often miss…

desk verdict A genuinely useful dataset aggregation effort whose benchmark section is currently invalid and whose isolated-atom energy normalization lacks the documentation and validation needed to trust it. read the letter →

arxiv 2411.19629 v1 pith:DEIIPAMO submitted 2024-11-29 physics.chem-ph cs.LG

Cristian Gabellini , Nikhil Shenoy , Stephan Thaler , Semih Canturk , Daniel McNeela , Dominique Beaini , Michael Bronstein , Prudencio Tossou This is my paper · ORCID
classification physics.chem-phcs.LG
keywords quantumchemistrydatasetsmachinelearninginteratomicpotentialsdatasetstandardizationenergynormalizationmulti-fidelitymethodsmemory-mappedstoragemoleculardynamics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims to consolidate 37 published quantum-mechanical datasets, covering over 400 million molecular geometries and more than 250 distinct QM method/basis-set combinations, into one programmatically accessible, ML-ready repository. It argues this removes a major barrier for machine-learning interatomic potential (MLIP) development, since QM data were previously scattered across repositories with inconsistent formats, units, and missing metadata. The work supports the repository with a Python library that standardizes loading, unit conversion, QM-method naming, and energy normalization, and with baseline experiments on SchNet, DimeNet, and TorchMD-Net. A careful reader should care because the claim is that a single library now gives the MLIP community the kind of standardized data access that has driven progress in other machine-learning fields.

What carries the argument

The machinery is a memory-mapped storage layout plus a QM-method registry. Geometries, atomic charges, positions, per-geometry index ranges, energy labels, and force labels are stored in memory-mapped arrays that allow indexing and batching without loading data into RAM. QM methods are normalized into Python enums composed of a functional, a basis set, and a correction method, which standardizes naming across datasets and enables studying basis-set or correction effects. For energy normalization, the library pre-computes per-atom-type mean energies via linear or ridge regression and a per-atom residual scale, and it stores computed isolated-atom energies so users can convert potential energies to atomization energies while preserving extensivity.

What would settle it

Recompute isolated-atom energies for a sample of the QM methods in the repository using an independent electronic-structure code and compare them against the stored values; a disagreement beyond numerical tolerance, say greater than 1 meV per atom, would propagate directly into the energy labels users train on. Checking a dataset's distance-unit metadata against its original publication would similarly confirm whether the conversions that feed radial cutoffs are trustworthy.

Watch

Extended reading notes

Core claim

The central discovery is that the fragmented landscape of QM datasets can be unified at scale: 37 datasets, hundreds of millions of geometries, 70 atom types, and 250+ quantum methods are re-exposed through one interface with harmonized metadata. The paper further claims that normalization matters physically: converting potential energies to atomization energies by subtracting computed isolated-atom energies preserves size extensivity, while naive Z-scoring violates it. On the benchmark side, the paper reports that well-known architectures such as SchNet, DimeNet, and TorchMD-Net frequently fail to reach chemical accuracy of 1 kcal/mol on these datasets, indicating headroom for new architectures.

Load-bearing premise

The computed isolated-atom energies and unit metadata for QM methods whose original datasets lacked them are correct; if they are wrong, the atomization-energy normalization and every energy benchmark built on it are wrong.

Editorial extensions

If this is right

  • Any of the 37 datasets can be loaded in one line of Python with user-specified energy, distance, and array formats, while original units are preserved for conversion.
  • Multi-fidelity training is supported because the same molecular families appear at multiple levels of theory, such as QM9 and MultiXCQM9 with its 228 DFT variants.
  • Energy normalization via isolated-atom subtraction lets models trained on one dataset be applied to different system sizes without breaking size extensivity.
  • Interaction-energy datasets such as DES370K, DES5M, Splinter, Metcalf, X40, and L7 provide a dedicated track for training models of drug-target, drug-drug, and protein-protein interactions.
  • Baseline results show SchNet consistently behind the other two architectures on potential-energy datasets while being competitive on interaction datasets, suggesting different architectural biases for the two task families.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's own admission that a bug affected dataset splits, making some random rather than molecule-based, implies the reported benchmark numbers should be treated as provisional until corrected re-runs appear.
  • If the computed isolated-atom energies are independently validated, the resulting harmonized multi-method repository would enable principled delta-learning and transfer-learning studies across levels of theory, which the paper only gestures toward.
  • The standardized method enum of functional, basis set, and correction opens a direct route to systematic studies of basis-set convergence and functional-family effects on learned potentials, going beyond what any single dataset paper provides.
  • One testable extension is to train two MLIPs on the same dataset with and without atomization-energy normalization and measure transfer error on larger molecules; the extensivity argument predicts the normalized model degrades less.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper introduces OpenQDC, a Python library and dataset repository that consolidates 37 existing quantum-mechanical datasets, spanning over 250 QM methods and roughly 400 million geometries, into a standardized, programmatically accessible format for machine-learned interatomic potential (MLIP) training. The authors describe the data storage and loading infrastructure, unit handling, QM-method notation standardization, and energy normalization based on isolated-atom reference energies. They also report baseline MLIP experiments with SchNet, DimeNet, and TorchMD-Net on a subset of these datasets, presenting a leaderboard of mean absolute errors. The paper explicitly acknowledges a bug in the dataset splitting that contaminated some baseline results, and notes that corrected results are forthcoming.

Significance. If the repository and preprocessing are correct, OpenQDC would be a valuable community resource: it offers a single, large, multi-fidelity corpus of QM data with a clean Python interface, which could lower the barrier for MLIP research and support multi-task and transfer-learning studies. The paper is also honest about its limitations, including the known split bug. However, the scientific value of the benchmark results is currently weakened by that bug, and the central preprocessing step of computing isolated-atom energies for all QM methods is undocumented and unvalidated. The manuscript therefore has a plausible and potentially impactful core, but the evidence presented is not yet sufficient for its claims.

major comments (3)
  1. [Section 4 (The OpenQDC Library), paragraph on metadata and isolated atom energies] The manuscript states that most datasets lacked isolated-atom energies and that the authors 'computed these values for all QM methods in the repository.' These values underpin the atomization-energy normalization that the paper advertises as a physics-motivated, size-extensive approach, so an error in any one of them propagates into every energy label and derived benchmark. The paper does not report the computational protocol (software, basis set, spin-state treatment, charge handling, convergence criteria), does not list which methods were computed versus taken from the original sources, and provides no validation against known reference values (e.g., QM7/QM9 atomization energies or original dataset metadata). Please add this protocol and a validation table, or state explicitly which datasets rely on externally provided reference energies.
  2. [Section 5.2 (Results) and Table 2] The paper admits that 'a bug affected the dataset splits: some were random rather than molecule-based as intended,' yet Table 2 reports numerical MAE values and the surrounding text draws comparative conclusions, such as SchNet being 'consistently and significantly outperformed' by TorchMDNet and DimeNet and SchNet being more competitive on interaction datasets. These conclusions are not supported by the current, contaminated results. The authors should either remove the benchmark table and defer all architectural comparisons until the corrected splits are available, or clearly label Table 2 as preliminary and confine the discussion to the corrected experiments.
  3. [Section 4 (Dataset Storage) and Section 3 (Table 1)] The abstract and Table 1 present exact counts of geometries, energy labels, and force labels, but the paper provides no checksums, hash-based integrity verification, or scripts that reproduce these statistics from the downloaded source archives. Given that the central claim is the creation of a trustworthy, standardized data commons, adding a reproducibility appendix with dataset-manifest hashes and a count-validation script would materially strengthen the paper; currently these counts are unverifiable from the manuscript alone.
minor comments (6)
  1. [Section 3.1 and Section 3.2] The text contains unresolved placeholder references 'Appendix ??' for dataset visualizations; these should be fixed or removed before publication.
  2. [Section 3, Table 1] The table heading contains a typo, 'min-manximum', and the column header 'Atom Min/-Max' is unclear; please use 'Atom Min/Max' and correct the typo.
  3. [Section 5.2] The word 'significently' should be 'significantly'.
  4. [Section 2 (Related works)] The phrase 'website (to be release upon acceptance)' is grammatically incorrect; it should read 'to be released upon acceptance.'
  5. [Section 5.1 and Section 5.2] The statement in Section 5.1 that 'For all datasets, we performed a molecule/system-based split' is directly contradicted by the split-bug disclosure in Section 5.2; please reconcile these statements in the revised manuscript.
  6. [Section 5.2] The sentence 'Accordingly, we will only compare different architectures and discuss differences based on chemical space only after correcting the splits' suggests the results in Table 2 should be explicitly labeled as provisional; currently the text draws preliminary conclusions despite this caveat.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper aggregates external datasets and reports empirical benchmark errors; its normalization constants are preprocessing, not predictions.

full rationale

This is a dataset aggregation and benchmarking paper rather than a derivation chain. The only constructed numerical quantities are the isolated-atom reference energies and the per-atom shift/scale statistics used in Eq. (1). Neither is presented as a prediction of the benchmark results, and the reported MAEs are measured on held-out molecular splits using labels transformed by these preprocessing constants, which is standard normalization rather than a restatement of the input data. The computed isolated-atom energies are indeed a load-bearing and underdocumented assumption, as the skeptic notes, but missing validation is a correctness risk, not circularity: nothing in the paper defines those energies in terms of the benchmark outputs, nor are they fitted to the test labels. References to prior work, including the coauthor citation [33], are contextual and are not load-bearing for the central aggregation or benchmark claims; no self-citation chain forces the results. The acknowledged split bug in Table 2 is a self-reported benchmarking flaw with no circularity implications. Accordingly, no specific circular step can be quoted or exhibited, and the appropriate finding is no significant circularity.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central claim is an infrastructure claim, so it rests on the correctness of the ingest/conversion process and the computed normalization constants rather than on a derived law. No free parameters are introduced for a scientific conclusion; the per-atom shifts and scales in Eq. 1 are data-fitting tools within the library.

free parameters (1)
  • per-atom energy shift mu_j,k and scale sigma_j per dataset/QM method = not reported; computed via linear/ridge regression
    Used in Eq. 1 to normalize energy labels before training; because these values are fit to the same data being predicted, benchmark MAEs depend on them. They are part of the library's normalization tooling, not of a scientific derivation.
assumptions (3)
  • domain assumption The original third-party QM datasets are correctly fetched, parsed, and converted to the unified memory-mapped format.
    Section 4 describes fetching and preprocessing but provides no verification (checksums, audit). Any conversion error would silently corrupt downstream MLIP training.
  • domain assumption The isolated atom energies computed by the authors for each QM method are correct.
    Section 4: 'Most datasets lacked this information, so we computed these values for all QM methods.' These values are essential for atomization-energy normalization and Eq. 1.
  • domain assumption The energy/force labels in the original datasets are accurate as published.
    The paper does not re-derive or check any QM labels; it trusts the source publications, which is standard for dataset aggregation.

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Cite this review

Pith. "Pith review of OpenQDC: Open Quantum Data Commons." pith.science (2026). https://pith.science/paper/DEIIPAMO

@misc{pith2026241119629,
  author       = {Pith},
  title        = {Pith review of: OpenQDC: Open Quantum Data Commons},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DEIIPAMO}},
  note         = {Machine review of arXiv:2411.19629}
}
read the original abstract

Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and force calculations. However, Quantum-Mechanical (QM) datasets, crucial for MLIPs, are fragmented across various repositories, hindering accessibility and model development. We introduce the openQDC package, consolidating 37 QM datasets from over 250 quantum methods and 400 million geometries into a single, accessible resource. These datasets are meticulously preprocessed, and standardized for MLIP training, covering a wide range of chemical elements and interactions relevant in organic chemistry. OpenQDC includes tools for normalization and integration, easily accessible via Python. Experiments with well-known architectures like SchNet, TorchMD-Net, and DimeNet reveal challenges for those architectures and constitute a leaderboard to accelerate benchmarking and guide novel algorithms development. Continuously adding datasets to OpenQDC will democratize QM dataset access, foster more collaboration and innovation, enhance MLIP development, and support their adoption in the MD field.

Figures

Figures reproduced from arXiv: 2411.19629 by the authors.

Figure 1
Figure 1. Overview of a dataset and its structure. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Inference time over the potential datasets. [PITH_FULL_IMAGE:figures/full_fig_p018_2.png] view at source ↗

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.