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Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models

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

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Pith's one-line read A model that never saw platinum still ranks it best for hydrogen evolution.

desk verdict The dataset is a real gift to the community; the Sabatier volcano is a visualization artifact that overstates what a linear model can do. read the letter →

arxiv 2411.11783 v1 pith:RIBR3EEV submitted 2024-11-18 cond-mat.mtrl-sci physics.chem-ph

Jehad Abed , Jiheon Kim , Muhammed Shuaibi , Brook Wander , Boris Duijf , Suhas Mahesh , Hyeonseok Lee , Vahe Gharakhanyan
show 12 more authors
Sjoerd Hoogland Erdem Irtem Janice Lan Niels Schouten Anagha Usha Vijayakumar Jason Hattrick-Simpers John R. Kitchin Zachary W. Ulissi Aaike van Vugt Edward H. Sargent David Sinton C. Lawrence Zitnick
This is my paper · ORCID
classification cond-mat.mtrl-sciphysics.chem-ph
keywords OCx24datasethydrogenevolutionreactionSabatiervolcanoadsorptionenergieshigh-throughputexperimentationmachinelearningCO2reductioncatalystscreening
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 builds a bridge between computational catalysis and high-throughput experiments by releasing a dataset (OCx24) of 572 synthesized samples, 441 tested electrodes, and adsorption energies for six adsorbates on 19,406 materials computed with an AI-accelerated pipeline. Its central claim is that a linear model trained on measured HER voltages using only the mean adsorption energies of hydrogen and hydroxyl can generalize to the full computational space and recover a Sabatier volcano with platinum at the peak, even though no platinum-containing sample was in the training data. If true, this supports adsorption energies as generalizable descriptors for HER and suggests that modest experimental sets can anchor computational screens of large material spaces.

What carries the argument

The load-bearing object is the mean adsorption energy E_ads,mean (Eq. 2), the arithmetic average of adsorption energies across all enumerated surface terminations up to Miller index 2. This single surface-level aggregation, combined with a linear regression trained on 179 experimental targets, allows inference over the full 19,406-material space; the Sabatier volcano emerges as the model's predicted activity landscape, and the paper's key comparison is the failure of Matminer-only features to reproduce the platinum apex.

What would settle it

Measure HER cell voltage for a set of the 436 predicted low-cost non-noble-metal candidates (e.g., Mo-S, Mo-Se alloys) under the same MEA conditions; if these materials do not outperform or match the copper baseline or platinum, the ranking is refuted. Alternatively, computing adsorption energies with Wulff or Boltzmann weighting for a subset of materials and checking whether platinum stays at the apex would test the mean-aggregation assumption directly.

Watch

Extended reading notes

Core claim

On the paper's own terms: using OCx24's experimental HER voltages at 50 mA/cm² as targets and the mean adsorption energy across all surface terminations (Eq. 2) of H and OH as features, a linear model trained under leave-one-composition-out cross-validation reaches R² = 0.59. Applied to the 19,406 stable and metastable materials from Materials Project, OQMD, and Alexandria, the model's predictions form a Sabatier volcano, with platinum predicted near the apex despite platinum being absent from the training set even as an alloy. The same model trained on Matminer bulk-element features reaches similar in-sample correlation but fails to place platinum at the top, indicating that adsorption energies carry the transferable signal. The paper also reports that CO2RR product rates are far harder to predict, with near-zero LOCO correlation, and attributes this to the reaction's network complexity and the limited expressiveness of single-adsorbate descriptors.

Load-bearing premise

The single mean adsorption energy over all computed surface terminations represents the catalytically relevant behavior of the real experimental nanoparticles; if that average is not representative, the predicted rankings of all 19,406 materials, including platinum's top position, lose their basis.

Editorial extensions

If this is right

  • The 3,869 materials predicted within twice the mean absolute error of platinum, including 436 that contain no noble metals, become concrete candidates for experimental HER testing.
  • Adding more experimental targets should sharpen the mapping; the paper projects from its scaling curve that 10⁴–10⁵ samples would yield substantially more predictive models.
  • Adsorption energies of H and OH, aggregated as a simple mean, are shown to be more transferable descriptors than bulk elemental features for HER activity ranking.
  • The OCx24 dataset, with its matched XRD/XRF characterization, provides a common testbed for future models that map computational descriptors to experimental performance.

Reading between the lines

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

  • The volcano's recovery suggests that the linear map learned from 179 samples may be capturing a genuine physical trend rather than a compositional artifact, but the claim would be strengthened by testing a set of the predicted low-cost candidates in the same MEA setup.
  • The paper's weakest link—the unweighted mean over surfaces—could be probed directly by comparing predicted rankings against measurements on well-faceted nanoparticles where the Wulff shape is known; if weighted energies change Pt's ranking, the mean aggregation is a lucky accident.
  • A natural extension is to use the same pipeline for other reactions whose selectivity is controlled by a small number of adsorption-energy descriptors, with the expectation that the CO2RR failure is due to descriptor insufficiency rather than the experimental bridge itself.
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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 manuscript introduces OCx24, a high-throughput experimental dataset of 572 synthesized samples (441 gas diffusion electrode tests) for CO2RR and HER, with XRF/XRD characterization, combined with a computational screening of 19,406 materials using ML-accelerated adsorption energy calculations (AdsorbML). The authors train linear regression models to predict HER cell voltage from mean H and OH adsorption energies, reporting LOO R2=0.61 and LOCO R2=0.59, and apply the model to the 19,406 materials, claiming to recover a Sabatier volcano with Pt at its apex despite no Pt in the training data. For CO2RR, random forest models achieve LOO R2~0.4-0.5 but LOCO R2 near zero, which the authors report transparently. The paper provides code and data under open licenses.

Significance. The experimental dataset is a substantial community resource: it is large, characterized, and measured under industrially relevant conditions, and the code/data are openly released. The LOCO HER correlation (R2=0.59) is a credible benchmark for composition-out-of-distribution prediction. The computational screening effort is the largest of its kind and will be useful for future descriptor studies. However, the headline claim that the model 'recovers a Sabatier volcano' with Pt at its apex is not supported by the analysis as presented, because the HER model is linear and the volcano is a density artifact. The dataset and screening remain valuable independent of that claim.

major comments (3)
  1. [Section 5.1, Eq. (5), Figure 4b] The claim that the model 'recovers a Sabatier volcano' is not supported by the analysis. The HER model is linear in the mean H and OH adsorption energies (Eq. 5 with f linear); a linear function has no interior maximum in the descriptor plane. The dome-shaped envelope in Figure 4b arises from the joint distribution of the 19,406 inference materials and the PC1 projection (e.g., the density of materials in the E_H–E_OH plane), not from a non-monotonic learned response. To support the volcano claim, the authors should fit a model with an explicit nonlinearity (e.g., quadratic in E_H and E_OH) and show that the data support an interior optimum, or reframe the volcano as a property of the inference-set distribution rather than the learned mapping.
  2. [Section 5.1, Pt extrapolation] The identification of Pt as a top HER catalyst is a single extrapolation. The training set (179 targets) is overwhelmingly Cu-based and occupies a compact feature region, while Pt's H and OH adsorption energies lie far outside that region. The LOCO R2=0.59 tests interpolation among compositions similar to the training set, not extrapolation to Pt. The paper reports no confidence intervals, bootstrap errors, or applicability-domain analysis for the 19,406 inference predictions, so the Pt ranking has unknown uncertainty. Please provide uncertainty quantification for the Pt prediction (e.g., conformal intervals or a bootstrap over the training data) and an assessment of feature-space overlap between training and inference sets, or soften the claim accordingly.
  3. [Section 5.1, Figure 4b, inference-set sensitivity] The peak position in Figure 4b is sensitive to two choices that are not tested: the Pourbaix stability filter that determines the 19,406-material inference set (Section 4.1) and the PC1 projection used for visualization. A linear model's predicted voltage for a material is a linear function of the descriptors, so the material that attains the maximum depends on the convex hull of the inference-set feature distribution. The claim that Pt is 'at the peak' should be robust to reasonable changes in the inference-set construction (e.g., different Pourbaix thresholds, or removing chalcogenides) and to the projection (e.g., plotting against E_H or E_OH directly). The authors should demonstrate this robustness or restrict the claim to the specific screen performed.
minor comments (6)
  1. [Abstract/Title] The abstract and title use 'OCX24' and 'OCx24' inconsistently; please standardize the dataset name throughout.
  2. [Section 2.2] The abbreviation 's.c.c.m.' should be 'sccm' (standard cubic centimeters per minute).
  3. [Section 5.1] The phrase 'within twice the mean absolute error (MAE) of Pt or better' is ambiguous; please specify the exact inequality used to define the 3,869 materials.
  4. [Section C.2.2] Citation markers such as 'GemNet-OC109' and 'AdsorbML13' need a space before the reference numeral in running text.
  5. [Section C.2.3] The sentence 'Vienna Ab initio Simulation Package (VASP) with projector augmented wave (PAW) pseudopotentials and the RPBE functional were used' has a subject-verb agreement error; use 'was used.'
  6. [Figure 4 caption] The caption does not define PC1; add a definition or refer to the text in Section 5.1 where it is introduced.

Circularity Check

1 steps flagged · score 4.0 of 10

Pt ranking is a genuine out-of-sample prediction, but the claimed 'data-driven Sabatier volcano' is a projection artifact of a linear model and reduces to the inference-set distribution.

  1. other [Section 5.1 (HER results), Eq. 5, Fig. 4b]
    "In this high-density data regime, we recover a Sabatier volcano for the model trained on mean OH and H adsorption energies, with platinum sitting at its peak despite platinum not appearing in the training data even as an alloy."

    Eq. 5 is fit as a linear model in the two features E_H and E_OH, so the predicted voltage is a plane with no interior maximum and cannot itself produce a volcano. After PCA collapses the same two features into PC1, the predicted voltage is an affine function of PC1, so the scatter is a line; any dome in Fig. 4b is a kernel-density feature of the 19,406-material inference set and the projection, not of the learned mapping. The claimed 'recovery' of a Sabatier volcano therefore reduces by construction to the input descriptor distribution, not to a model-derived prediction.

full rationale

The core Pt identification is not circular: Pt was absent from the 179 experimental targets, its descriptors come from an independent AdsorbML+DFT pipeline, and the linear model's ranking of Pt is an out-of-sample extrapolation, not a fitted value. Self-citations to AdsorbML/GemNet-OC/OC20 are tool citations with published benchmarks and code, so they are independent support under the rules. The descriptor choice (H and OH) is informed by prior literature, but the experimental voltage labels are new and independent, so using prior knowledge for feature selection is not circular. The one circularity-adjacent issue is the 'Sabatier volcano' claim: with Eq. 5 linear, the volcano shape cannot come from the model; it is a visualization artifact of the inference-set distribution and PCA projection, i.e., the headline 'data-driven volcano' is a property of the input material set rather than a model prediction. This affects the framing of the result but not the independent content of the Pt out-of-sample ranking, so the score is moderate rather than high.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central claims rest on the accuracy of the ML-plus-single-point-DFT adsorption energies, the validity of the mean-surface aggregation, and the Pourbaix stability filtering. The experimental data are independent and the regression is fit to them, so the fitted coefficients are free parameters. No new physical entities are introduced.

free parameters (5)
  • HER linear model coefficients = not reported in paper
    Coefficients mapping mean H and OH adsorption energies to predicted HER voltage, fitted to 179 experimental targets.
  • XRD matching thresholds = Rwp<=40, q>=70, weight fraction >=70%
    Hand-chosen acceptance criteria for phase matches in Section 3.2; they determine which 43 samples are 'matched'.
  • Pourbaix stability threshold = 0.05 eV/atom
    Hand-chosen cutoff for decomposition energy in Section 4.1; determines the 19,406 materials screened.
  • q-score scaling alpha = 0.05
    Exponential scaling factor in Eq. 1 for XRD match quality.
  • Fixed interpolation potential = 3.3 V
    Average full-cell voltage used to interpolate CO2RR production rates to a fixed potential (Section C.3).
assumptions (5)
  • domain assumption Adsorption energies computed by ML relaxation plus DFT single points (AdsorbML) approximate true energetics well enough for descriptor-based modeling.
    Section C.2.2: only single-point DFT on ML-relaxed structures, no full DFT relaxations; accuracy on 19,406 diverse materials is not quantified.
  • domain assumption The mean adsorption energy over all surfaces up to Miller index 2 represents the catalytically relevant sites of nanoparticles.
    Section 4.3, Eq. 2; Wulff and Boltzmann weightings did not improve predictions.
  • domain assumption The Pourbaix framework with linear formation-energy corrections across databases selects materials stable under reaction conditions.
    Section C.1; corrections fitted to MP-analogous entries, magnetic moments removed from Alexandria materials.
  • domain assumption Log-linear interpolation of production rates to a fixed potential of 3.3 V is valid when the target lies within the tested current density range.
    Section C.3; extrapolation is clipped to range endpoints, which may bias targets.
  • domain assumption Standard DFT (RPBE) and VASP pseudopotentials give reliable adsorption and cleavage energies for these materials.
    Used throughout the computational pipeline; errors are not propagated to the final predictions.
invented entities (1)
  • None
    purpose: No new physical entities are introduced.
    The 'Sabatier volcano' is a visualization of a fitted linear model, not a new theoretical construct.

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Cite this review

Pith. "Pith review of Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models." pith.science (2026). https://pith.science/paper/RIBR3EEV

@misc{pith2026241111783,
  author       = {Pith},
  title        = {Pith review of: Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RIBR3EEV}},
  note         = {Machine review of arXiv:2411.11783}
}
abstract

The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new catalysts is currently limited by the gap between what AI-accelerated computational models predict and what experimental studies produce. To make progress, large and diverse experimental datasets are needed that are reproducible and tested at industrially-relevant conditions. We address these needs by utilizing a comprehensive high-throughput characterization and experimental pipeline to create the Open Catalyst Experiments 2024 (OCX24) dataset. The dataset contains 572 samples synthesized using both wet and dry methods with X-ray fluorescence and X-ray diffraction characterization. We prepared 441 gas diffusion electrodes, including replicates, and evaluated them using zero-gap electrolysis for carbon dioxide reduction (CO$_2$RR) and hydrogen evolution reactions (HER) at current densities up to $300$ mA/cm$^2$. To find correlations with experimental outcomes and to perform computational screens, DFT-verified adsorption energies for six adsorbates were calculated on $\sim$20,000 inorganic materials requiring 685 million AI-accelerated relaxations. Remarkably from this large set of materials, a data driven Sabatier volcano independently identified Pt as being a top candidate for HER without having any experimental measurements on Pt or Pt-alloy samples. We anticipate the availability of experimental data generated specifically for AI training, such as OCX24, will significantly improve the utility of computational models in selecting materials for experimental screening.

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Forward citations

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Pith tools

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