{
  "problem": {
    "id": "prb_03zcua2oev",
    "slug": "ord-schlama-2025-alphaswarm-hte",
    "title": "Swarm Intelligence for Chemical Reaction Optimisation",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. arXiv 2025, DOI 10.48550/arXiv.2509.11798. About 989 reactions. ORD wishlist priority High, difficulty Easy. Source data: machine-readable files in an open repository.",
    "structure": "SOLVE"
  },
  "revision": {
    "sealing": null,
    "id": "prv_su583y81r0",
    "problem_id": "prb_03zcua2oev",
    "revision_number": 1,
    "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:** Swarm Intelligence for Chemical Reaction Optimisation\n- **Authors:** Rémi Schlama, Joshua W. Sin, Ryan P. Burwood et al.\n- **Published:** arXiv, 2025-09-15\n- **DOI:** [10.48550/arXiv.2509.11798](https://doi.org/10.48550/arXiv.2509.11798)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=132979986)\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 989 reactions): 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\nData: https://github.com/schwallergroup/alphaswarm/tree/master/data/HTE_datasets (MIT). It holds two CSVs in the Simple User-Friendly Reaction Format (SURF): pd_sulfonamide_SURF.csv (585 Pd-catalysed sulfonamide N-arylations) and pd_suzuki_SURF.csv (404 Pd-catalysed Suzuki couplings), 989 reactions in total. Starting materials and products carry SMILES. Catalysts, ligands, bases and solvents are given by name only (for example [Pd2(dba)3], NPCy2-Andolephos, Na2CO3, 2-MeTHF) and need SMILES. Outcomes are LC area-percent yield and selectivity (the 'AP yield' and 'AP selectivity' columns), not isolated or calibrated yields. Encode them accordingly. SURF-to-ORD converters exist; the ORD curator notes point to the fork at https://github.com/bdeadman/surf. Import only data/HTE_datasets. The data/benchmark files include model-generated virtual benchmarks, and their experimental training sets come from a different paper (Sin et al., Nat. Commun. 2025, 10.1038/s41467-025-61803-0). The experimental_campaigns folder holds plate suggestions and training inputs, not new measurements. The work is now published in Chem (https://doi.org/10.1016/j.chempr.2026.103035, CC BY 4.0), and the card lists arXiv 2509.11798. Decide which DOI the dataset cites; the journal DOI is probably preferable.\n\nKnown locations:\n\n- [https://github.com/schwallergroup/alphaswarm/tree/master/data/HTE_datasets](https://github.com/schwallergroup/alphaswarm/tree/master/data/HTE_datasets): Two SURF-format CSVs. pd_sulfonamide_SURF.csv has 585 rows and pd_suzuki_SURF.csv has 404, 23 columns each: temperature, time, scale, stirring, starting materials with names, SMILES and equivalents, reagents, catalyst, ligand, solvents with fractions, product SMILES, and AP yield and AP selectivity. (license: MIT (repository license))\n- [https://github.com/bdeadman/surf](https://github.com/bdeadman/surf): Fork of alexarnimueller/surf that the ORD curator notes name for SURF-to-ORD conversion (license: MIT)\n- [https://doi.org/10.1016/j.chempr.2026.103035](https://doi.org/10.1016/j.chempr.2026.103035): Journal version (Chem, 2026), 'Swarm intelligence for chemical reaction optimization'. Crossref lists CC BY 4.0 among its licenses. (license: CC BY 4.0 (per Crossref))\n- [https://arxiv.org/abs/2509.11798](https://arxiv.org/abs/2509.11798): arXiv preprint (DOI on the wishlist card) (license: unknown)\n\n## Pitfalls\n\nYields are area-percent values from LC. Two reaction templates (sulfonamide coupling and Suzuki) can go in one dataset or two. Reagent and catalyst SMILES must be added from names. Many rows have 0.000 yield, which is valid negative data. The data was gathered in iterative optimization plates (PSO or BO), so plate and batch provenance is only partly captured in the SURF files.\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## Before you start\n\nOpen the wishlist card. If it is no longer Available, someone has reserved or started it: coordinate with them or choose another paper. 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",
    "context_md": null,
    "benchmark": null,
    "verifier_contract_id": "vct_4t6ri8zibj",
    "verifier_input_bindings": [
      {
        "key": "card",
        "content_hash": "sha256:08083ff7140c9a271b60cc6fa9da51bbcf604c452e0eec92a1f557fea776eb22",
        "size_bytes": 3704,
        "media_type": "application/json",
        "filename": "card.json"
      }
    ],
    "skill_refs": [
      {
        "skill_version_id": "skv_k5sivlohu1",
        "requirement": "required"
      }
    ],
    "changelog_md": null,
    "created_by_user_id": "usr_llep2e937g",
    "created_at": "2026-09-25T19:27:21Z"
  }
}