Predict HER activity for element systems the model has never seen

The paper's linear model on mean H and OH adsorption energies reaches a leave-one-composition-out R² of about 0.59 on the 2024 data. Build something better, and prove it under the Campaign's fixed protocol.

The task

• Targets: voltage_she_v in targets_her.csv from prepare_targets.py run on experiments and xrf (280 rows, 52 element systems on the bound data). Report the SHA-256 of the file.
• Folds: leave-one-composition-out by element system (elements), every row of the held-out system predicted by a model that saw none of its electrochemistry. Hyperparameters fixed a priori or chosen inside the training folds.
• Metric: pearson_r2 from evaluate.py --scheme loco; also report r2, mae and spearman_rho, and the LOO numbers if you like.
• Inputs you may use: the compositions (XRF and target), source, public computational data and models (the OCx24 descriptor file, OC20/OC22/OC25 or OMat24 potentials, Materials Project, OQMD, Alexandria), simulations you run yourself, XRD phase information. Not the outcomes of held-out systems in any form (the ocx25-data Skill lists the rules).
• Include the baseline in your own harness: the paper's linear model on mean H and OH energies, as a models entry with is_baseline: true, on exactly the same targets and folds, so the comparison is like for like.

Acceptance bar

Verified when, on the canonical targets under LOCO, your best model reaches pearson_r2 ≥ 0.75 and exceeds the in-harness baseline by at least 0.10, the campaign reviewer reproduces both numbers by re-running your 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; a later revision may move it. The noise floor (σ ≈ 0.043 V vs SHE between replicates) bounds what is achievable; report MAE against it.

What to submit

• document: JSON satisfying document_schema (problem is her-model; all models tried that you want on record, baseline included; leakage_audit; reproduction with command, environment, external downloads and hashes; limitations).
• report: Markdown, under 3,000 words: features and why, model, the LOCO table against the baseline, where the model fails (which element systems), and what the lab should test to confirm it.
• code: an archive that re-runs from the bound inputs and listed downloads and regenerates every predictions table (sample_id, prediction, fold).

Submissions that improve on the baseline without reaching the bar are welcome as Attempts with Notes and Artifacts: they are how the next solver starts further along. Bound inputs: experiments, xrf, dashboard, document_schema.