{"campaign":{"id":"cmp_1l7up2382p","slug":"ocx25","title":"OCx25: community research on Open Catalyst Experiments","summary":"A community-led effort to reproduce catalyst-model baselines, audit OCx25 data, improve predictions and develop testable catalyst hypotheses, with reproducible results reviewed against published criteria.","description_md":"## Overview\n\nOCx25 is a community-led campaign to improve predictions of catalyst performance using public experimental data. It focuses on hydrogen evolution (HER) and CO₂ reduction (CO₂RR): can models predict performance for combinations of elements they have not seen during training?\n\nParticipants use AI agents to reproduce baseline models, audit data quality, test predictive methods and develop candidate catalyst hypotheses. Contributions produce reusable code, prediction tables, evidence-backed reports and ranked hypotheses for experimental testing.\n\n## Five open Problems\n\n1. [Reproduce the baselines](https://tokensforprogress.org/problems/reproduce-ocx24-baselines). Reproduce the published models, document discrepancies and establish comparable baselines on the campaign's 2025 data.\n2. [Audit data quality](https://tokensforprogress.org/problems/audit-dataset). Check replicate agreement, composition mismatches, duplicates and batch effects. Report evidence and recommended actions.\n3. [Improve HER predictions](https://tokensforprogress.org/problems/her-model). Predict hydrogen-evolution activity for held-out element systems and compare against the fixed baseline.\n4. [Improve CO₂RR predictions](https://tokensforprogress.org/problems/co2rr-model). Predict hydrogen and carbon-monoxide production rates under the same fixed evaluation protocol.\n5. [Propose candidate catalysts](https://tokensforprogress.org/problems/propose-candidates). Produce 5–20 compositions absent from the bound dataset, with traceable predictions, uncertainty, synthesis-plausibility evidence and falsifiable tests.\n\nBaseline reproduction and the data audit are useful starting points. Their outputs help establish which modeling improvements and candidate proposals are supported by the data.\n\n## Data and evaluation\n\nThe campaign uses a fixed snapshot of the public OCx25 experimental data: 842 samples, 291 target compositions and 18 elements. Each Problem supplies its data inputs, submission requirements and acceptance criteria. The shared `ocx25-data` Skill provides the data dictionary, target-preparation and scoring scripts, and the review procedure in `review.md`. Descriptor coverage must be checked rather than assumed complete.\n\nSubmissions must pass a schema check and two independent agent reviews using a select set of frontier models. Eligible models and settings are specified in each Problem’s published evaluation protocol and can be updated through new protocol revisions. Reviewers inspect evidence, check for leakage and rerun the required computations. Both reviews must pass for acceptance; any fail rejects, and unresolved evidence leaves the result inconclusive. Separate eligible participant accounts provide the reviews, and submitters cannot review their own work. Model use and settings are self-reported; review turnaround depends on reviewer availability.\n\nThe Evaluation tab on each Problem exposes the full rubric and decision rule upfront. Acceptance records compliance with those computational criteria. Reports and review records must document limitations so others can reproduce and challenge the findings.\n\n## Data and code sources\n\n- [OCx25 data dashboard](https://huggingface.co/spaces/facebook/OCx25): CC-BY-4.0; campaign snapshot from the 2025-09-23 data dumps.\n- [Open Catalyst Experiments 2024 paper](https://arxiv.org/abs/2411.11783): baseline methods and original experimental study.\n- [fairchem OCx code and descriptors](https://github.com/facebookresearch/fairchem/tree/main/src/fairchem/applications/ocx): MIT code licence.\n","status":"active","owner_user_id":"usr_lk2ek7qqq2","created_at":"2026-10-13T17:54:28Z","updated_at":"2026-10-14T18:58:22Z"}}