Predict CO₂ reduction selectivity for element systems the model has never seen

This is the Problem the dataset was built for. On the 2024 data, random forests on aggregated adsorption energies and elemental features reach a leave-one-composition-out R² of about 0.22 for the H₂ production rate and about 0 for CO. The paper's discussion points at what lone-adsorbate descriptors on clean surfaces leave out (competing pathways, electrolyte, pH, field, interface, real phases) and at the dataset's size. Improve on the baselines with whatever physics, simulation or learning you can bring, and prove it under the fixed protocol.

The task

• Targets: h2_pr and co_pr (production rates at 3.3 V, mol cm⁻² s⁻¹) in targets_co2rr.csv from prepare_targets.py on experiments and xrf (279 rows, 52 element systems). Also report liquid_pr, and the Faradaic-efficiency columns if your model predicts them. Report the SHA-256 of the file.
• Folds: leave-one-composition-out by element system. Hyperparameters fixed a priori or chosen inside the training folds. One model per target or one multi-output model; say which.
• Metric: pearson_r2 from evaluate.py --scheme loco on the linear production rates (the paper's convention); also report the --log-target scores, r2, mae and spearman_rho.
• Inputs you may use: compositions (XRF and target), source, public computational data and models, simulations you run, XRD phases, the literature about materials. Not the outcomes of held-out systems in any form. The ocx25-data Skill has the rules; the HER outcomes of the same samples are outcomes too and are off limits for a held-out system.
• Include the baseline in your harness: the paper's random forest (scikit-learn defaults, random_state=42) on Boltzmann-weighted energies plus Matminer features, as models entries with is_baseline: true.

Acceptance bar

Verified when, on the canonical targets under LOCO, one model reaches pearson_r2 ≥ 0.50 for h2_pr and ≥ 0.40 for co_pr, each exceeding the in-harness baseline by at least 0.15; the campaign reviewer reproduces the numbers by re-running the code and re-scoring the predictions tables with evaluate.py; and the leakage audit holds. The reviewer may also run your submitted code on measurements that are not in this release and will report that score alongside yours, so selecting a model on the public data will not pay. The bar is the Campaign owner's and a later revision may move it. Partial results (one target, a smaller gain, a diagnosis of why a class of features cannot work) belong in Notes and Artifacts on your Attempt so the next solver starts from them.

What to submit

• document: JSON satisfying document_schema (problem is co2rr-model; every model you want on record, baselines included; leakage_audit; reproduction; limitations).
• report: Markdown, under 4,000 words: the physics or data you added and why it should matter, the model, the LOCO table against the baseline per target, which element systems it gets wrong and a hypothesis why, and the five compositions you would test first to confirm the model.
• code: an archive that re-runs from the bound inputs and listed downloads with hashes, and regenerates every predictions table (sample_id, prediction, fold).

Bound inputs: experiments, xrf, dashboard, document_schema.