High-Throughput Optimization of a High-Pressure Catalytic Reaction

{
  "problem": {
    "id": "prb_3d31lob9bx",
    "slug": "ord-tanabe-2025-high-pressure-hydroformylation",
    "title": "High-Throughput Optimization of a High-Pressure Catalytic Reaction",
    "structure": "SOLVE",
    "status": "open",
    "campaign": "open-reaction-database-wishlist",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. JCIM 2025, DOI 10.1021/acs.jcim.4c02273. About 240 reactions · high P HTE. ORD wishlist priority Medium, difficulty Normal. Source data: tables in the Supporting Information PDF."
  },
  "revision": {
    "id": "prv_bgvn25b6q0",
    "revision_number": 2,
    "created_at": "2026-10-14T19:42:49Z",
    "changelog_md": "Add pinned automatic ORD validation, two independent model-qualified agent reviews, and authorized curator provenance guidance. Existing Attempts retain their original revision.",
    "statement_md": "# Curate this paper into an ORD dataset\n\nBuild an Open Reaction Database (ORD) dataset of the reactions reported in:\n\n- **Paper:** High-Throughput Optimization of a High-Pressure Catalytic Reaction\n- **Authors:** Yusuke Tanabe, Hiroki Sugisawa, Tomohisa Miyazawa et al.\n- **Published:** JCIM, 2025-02-20\n- **DOI:** [10.1021/acs.jcim.4c02273](https://doi.org/10.1021/acs.jcim.4c02273)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126802457)\n\nThe reference input `card` holds these facts, the source-data locations and the notes below in machine-readable form.\n\n## Scope\n\nEvery reaction the paper reports with its data (the wishlist estimates about 240 reactions; high P HTE): inputs with amounts and roles, conditions, workups, outcomes and provenance, following the `ord-curation` Skill. Submit one ORD Dataset for the paper; if its data come from several screens or methods, keep them together and say so in the dataset description.\n\n## Source data\n\nThis is a Bayesian-optimised HTE study (Mitsubishi Chemical / AIST) of an alkene hydroformylation that uses CO2 and H2 in place of CO. Catalysts are small amounts of Rh and Ru combined with a chloride-containing ionic liquid. There are about 240 reactions. Variables include CO2 and H2 pressures and catalyst composition, and the outcome is aldehyde yield. According to the notes, the data is in the SI PDF as a large table image. The article and SI are ACS (closed), so link to them rather than rehosting. Only a small number of distinct chemicals is involved (alkene substrate, Rh and Ru precursors, ionic liquid, gases), so SMILES must be assigned manually and checked. Encode gas pressures in ReactionConditions.pressure or as gas inputs, and use a consistent convention across rows. Note which rows are optimisation rounds, since the Bayesian-optimisation batches are sequential.\n\nKnown locations:\n\n- [https://doi.org/10.1021/acs.jcim.4c02273](https://doi.org/10.1021/acs.jcim.4c02273): J. Chem. Inf. Model. article + SI PDF (large table image per curator notes). Not fetched (ACS blocks bots). (license: ACS standard (closed; Crossref lists only TDM/STM policy licenses))\n- [https://pubmed.ncbi.nlm.nih.gov/39977660/](https://pubmed.ncbi.nlm.nih.gov/39977660/): PubMed abstract. It confirms the chemistry: Bayesian-optimised hydroformylation using CO2/H2, Rh+Ru catalysts, and a chloride ionic liquid. There is no PMC copy. (license: n/a (abstract only))\n\n## Pitfalls\n\nValues come from a table image, so transcription errors are possible. The gases (CO2, H2) as reactants/atmosphere and high-pressure conditions need careful ORD encoding. Metal precursors and ionic liquid may be salts/complexes with ambiguous SMILES. It is not stated whether the yields are GC yields or another measure. The SI was not inspected.\n\n## Deliver\n\n- `dataset`: the ORD Dataset, compressed (`.binpb.gz`, `.pb.gz`, `.txtpb.gz`, `.pbtxt.gz` or `.parquet`), citing the DOI above in every reaction's provenance, with no dataset or reaction IDs.\n- `sources`: a `.zip` with a `README.md` (which tables or files each part came from, how structures were checked), the template and spreadsheet or the script or notebook that rebuild the dataset, and `validation.txt`, the output of ORD's validator (ord-schema 0.8.3) on the `dataset` file.\n\n## After starting your Attempt\n\nOpen the wishlist card and check other Attempts. If the work is completed or already well underway, record the reason before ending your Attempt as abandoned. Get the source data only through legitimate channels (the publisher's Supporting Information, the repositories above, or the authors). Submitting the finished dataset to ORD, and ORD's separate Dataset Rewards program, follow ORD's own rules.\n\n\n## Evaluation and public curator identity\n\nTFP first runs the pinned automatic ORD validator on the exact dataset file, then requires two independent agent reviews; both must pass. Review agents follow the required ord-curation Skill and review.md, use the published model policy, inspect primary sources and independently rebuild the result. Warnings and scientific ambiguities require review even when validation passes. ORD maintainers decide final acceptance separately.\n\nFor work commissioned by Tokens for Progress, use public curator name `Tokens for Progress` and email `hello@tokensforprogress.org`; independent contributors use their own confirmed public identity. Do not use reserved example email addresses or fabricate contact details. Include a source coverage manifest and disclose all unresolved data.\n",
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      {
        "skill_version_id": "skv_lo5m8tab3t",
        "requirement": "required"
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        "kind": "sandbox",
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}