OCx25 reference

Provenance

• Experimental campaign: Meta FAIR Chemistry, University of Toronto (Sinton and Sargent groups) and VSParticle. Paper: Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models, arXiv:2411.11783 (Abed, Kim, Shuaibi, Wander, … Sinton, Zitnick). A follow-up on the 2025 data is expected in Nature Catalysis.
• Data bound here: the 2025-09-23 dumps from the OCx25 dashboard Space (ExpDataDump_250923.csv, XRFDataDump-250923.csv, DashboardData-250923.csv), licence CC-BY-4.0. The 2024 dumps (*_241125.csv) are in the same Space and the paper's exact 2024 files (ExpDataDump_241113.csv, XRDDataDump-241113.csv, processed HER_40_70_all.csv, CO2R_40_70_all.csv) are in fairchem applications/ocx/data.
• Computational descriptors: comp_df_241022.csv, 1.5 GB, MD5 9e75b95bb1a2ae691f07cf630eac3378, at https://dl.fbaipublicfiles.com/opencatalystproject/data/ocx24/comp_df_241022.csv. One row per surface of 19,406 bulk materials (Materials Project mp-, OQMD oqmd-, Alexandria dcgat- ids) with Miller indices up to 2: bulk_id, slab_millers, slab_shift, top, slab_comp, n_bulk_elements, per adsorbate (C, CO, CHO, COCOH, H, OH) {ads}_ml_Eads, {ads}_ml_dft_Eads, {ads}_min_ml_Eads, {ads}_min_sp_e (the DFT single-point verified minimum used by the paper), cleavage_energy (only for tested compositions), facet_fraction_on_wulff_not_normalized.
• XRD: raw .xy patterns and phase identifications for every sample are in the Space under Data/XRD/ (Git LFS) and, for 2024, in fairchem's XRDData_241116.tar.gz (19.6 GB). Fewer than a quarter of samples match their target phase at the paper's criteria (major phase ≥ 70 wt %, composition within 2σ of XRF, q-score ≥ 70, Rwp ≤ 40).

The 2025 data at a glance

Replicates: uoft samples share one XRF measurement per synthesis target; vsp samples are printed per replicate and carry their own XRF. Rows with rep = avg or std are summaries and are dropped by the protocol.

Experimental conditions (from the paper)

• Zero-gap membrane-electrode-assembly electrolyser, 9 minutes per current density; gas products by GC; liquid products estimated from the electron balance (assumed formate).
• HER at 50 mA/cm². Full-cell voltage is converted to cathode potential vs SHE with a calibration from a three-electrode cell (Arabyarmohammadi et al., Nature Sustainability 2025); prepare_targets.py ports it. The conversion is affine in the full-cell voltage.
• CO₂RR at 50, 100, 150, 200 and 300 mA/cm²; the paper compares materials at a fixed full-cell voltage of 3.3 V (the average across tests) by log-linear interpolation, without extrapolation.
• Synthesis: VSParticle VSP-P1 spark ablation (Ar/H₂ carrier, nanoporous films printed on Si wafers and gas-diffusion electrodes in parallel); University of Toronto Chemspeed robotic chemical reduction (hydrazine/NaOH, 50 °C, washed, dried), ink with Vulcan XC-72R and Nafion, optional anneal at 400 °C under 5 % H₂.

Published baselines (2024 data, paper Table 1; R² = Pearson r²)

HER, linear regression, target cathode potential vs SHE at 50 mA/cm²:

CO₂RR, random forest (scikit-learn defaults, random_state=42), target production rate at 3.3 V:

Faradaic efficiency as target (appendix): similar; CO LOO 0.55.

Matminer features used: composition-weighted mean of Pauling electronegativity, row, group, atomic mass, atomic radius and Mendeleev number, computed on the XRF composition; all features standardised.

Aggregation of surface energies to one number per material (paper Eqs. 2–4, data_handling.compute_comp_stats): simple mean over surfaces; Boltzmann weighting by adsorption energy and cleavage energy at 300 K; Wulff weighting by facet fraction with cleavage energy as surface-energy proxy; falling back to the mean when cleavage energies are missing.

Noise floors (replicates within ± 5 % XRF composition)

• HER: σ = 0.043 V vs SHE over 297 replicate pairs.
• CO₂RR production rate at 3.3 V: σ = 0.050 (H₂), 0.040 (CO), 0.033 (liquid) µmol cm⁻² s⁻¹.

A model cannot be expected to beat these, and a reported R² should be read against them. The paper's discussion of the CO₂RR results: many competing products, descriptors of lone adsorbates on clean surfaces, no electrolyte, pH, field, interface or reactor physics in the simulations, and only 179 targets; they estimate 10⁴–10⁵ samples for substantially better models. Independent commentary (arXiv:2512.05938) argues adsorption-energy descriptors are necessary but not sufficient and that uncertainty of 0.3–0.5 eV per predicted adsorption energy limits screening.

Differences between this protocol and the paper's pipeline

prepare_targets.py follows fairchem's load_and_preprocess_data, with these documented differences, chosen so that targets can be derived from the bound bytes alone:

• Replicates are pooled by synthesis target (sample id without _repN, per source) and the pooled row carries the element-wise mean of its replicates' XRF compositions. The paper pools by the nearest composition in the computational screening set, which needs the 1.5 GB file.
• No XRD matching is applied; the paper's best models also trained on all samples, matched or not, and found that better than matched only.
• Electron counts for production rates follow the reference code (2 for H₂, CO, liquid and C₂H₄; 8 for CH₄). The reference uses 2 for C₂H₄, whose physical value is 12; the convention is kept for comparability and C₂H₄ is not a headline target.

When you reproduce the paper's exact numbers (reproduce-ocx24-baselines), use the fairchem pipeline as published; when you compare models in this Campaign, use the canonical targets.

CO₂RR context a model may need

The cathode side runs at high local pH; H₂ competes with every carbon product; CO is the dominant carbon product on Ag/Au/Zn-rich surfaces, formate (reported as liquid) on Sn/In/Cd/Hg/Tl-rich ones, CH₄ and C₂H₄ appear mainly on Cu-rich surfaces and at low rates in this dataset. Many samples are phase-separated or oxidised rather than the intended alloy; treat the XRF composition as the catalyst's composition and the target composition as the recipe.