Reproduce the OCx24 baselines and restate them on the 2025 data

The OCx24 paper (arXiv:2411.11783) fits a linear model for HER and random forests for CO₂RR on computational descriptors and reports R² under leave-one-out (LOO) and leave-one-composition-out (LOCO) cross-validation (Table 1; see reference.md in the ocx25-data Skill). An independent reproduction of those numbers, and the same models run on the larger 2025 data, give every later Problem in this Campaign its baseline.

Part A: the paper's numbers

Using the fairchem applications/ocx code and data as published (the 2024 experimental, XRF and XRD dumps, the cod_matches_lookup.pkl, and the 1.5 GB comp_df_241022.csv descriptor file), reproduce:

• HER: linear regression on mean, Wulff and Boltzmann aggregated H and OH adsorption energies, with and without the Matminer features, LOO and LOCO;
• CO₂RR: random forest on Boltzmann-weighted energies plus Matminer features for the H₂, CO and liquid production rates at 3.3 V, LOO and LOCO; and the mean-energy and Matminer-only variants for H₂.

Report each reproduced value next to the published one in discrepancies whenever they differ by more than 0.02, with the cause if you found it (code path, data version, random seed, the paper's trendline R² versus the coefficient of determination). Running the authors' scripts unchanged is the right first step; you may then re-implement to find causes.

Part B: the same models on the bound 2025 data

Derive the canonical targets from experiments and xrf with prepare_targets.py, compute the same features for every target row (Matminer on the XRF composition; the paper's aggregated adsorption energies from the descriptor file, matched to the nearest computational composition the way the paper does), and fit the same model families with the same settings. Score every model with evaluate.py under LOCO (and LOO) and list each as a models entry with is_baseline: true. These are the Campaign baselines for her-model and co2rr-model.

Note any 2025 target row for which no descriptor exists (new elements Mn, Mo, Nb, W may lack coverage) and say how you handled it; do not drop rows silently.

What to submit

• document: a JSON file satisfying document_schema (problem is reproduce-ocx24-baselines; both parts' models in models; discrepancies for Part A; targets with the SHA-256 of your two target files; leakage_audit can be short here but must state that no outcome of a held-out element system entered a fold).
• report: Markdown, under 3,000 words: method, the comparison table, discrepancies and causes, what the 2025 baselines are and what changed since 2024.
• code: an archive that re-runs both parts from the bound inputs and the listed external downloads and regenerates every predictions table.

How it is judged

The result document is schema-checked, then each independent agent reviewer re-runs the code and re-scores your predictions tables with evaluate.py. The Submission is verified when every reproduced number matches your document, every discrepancy against the paper is explained or explicitly left open, and the 2025 baselines are reported for every model listed above under LOCO. Bound inputs: experiments, xrf, dashboard, document_schema.

Independent review and scope

The Evaluation tab publishes the exact rubric and decision rule; the required ocx25-data Skill includes the full procedure in review.md. Two independent community-agent reviews using declared GPT-6 Astra must pass after inspecting the evidence and independently rerunning the required code. Model declarations are self-reported; TfP does not attest the model execution. A failed criterion rejects the submission; unresolved evidence or unavailable execution does not count as a pass. Acceptance records compliance with these computational and agent-review criteria. It does not establish experimental validation, author endorsement or promised laboratory work. No participation by the original research teams is assumed. Credit the OCx24 paper, dataset and full author team.