{
  "source": "ORD Dataset Wishlist",
  "snapshot_date": "2026-09-25",
  "ord_wishlist_status_at_snapshot": "Available",
  "ord_wishlist_card": "https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570026",
  "ord_data_issue": null,
  "title": "Doing More with Less, On Time and In Full: An Intelligent Multiattribute Process Optimization Platform",
  "doi": "10.1021/acs.oprd.3c00392",
  "journal": "OPRD",
  "publication_date": "2024-02-26",
  "authors": [
    "Reihaneh Soleimany",
    "Wenyao Zhang",
    "Jee Seong Kwak",
    "Debasis Mallik",
    "Michael G. Organ"
  ],
  "article_open_access": {
    "status": "closed",
    "license": null
  },
  "dataset_size_estimate": null,
  "experiment_type": null,
  "ord_priority": null,
  "ord_difficulty": null,
  "source_data_kind": "author_request_needed",
  "smiles_in_source": "unknown",
  "needs_author_contact": true,
  "data_locations": [
    {
      "url": "https://doi.org/10.1021/acs.oprd.3c00392",
      "what": "OPRD article and Supporting Information; contents not verified",
      "license": "ACS publisher terms (closed article)"
    }
  ],
  "notes": "Paper: Soleimany et al., Org. Process Res. Dev. (published online 2024), doi:10.1021/acs.oprd.3c00392 (closed access). It describes an intelligent multiattribute process-optimization platform. The underlying experiment-level reaction data may not be in the SI; the curator needs to confirm whether per-experiment conditions and outcomes are tabulated. If they are not, the data must come from the authors. Optimization campaigns often record several responses per experiment (yield, impurity levels and so on), and each should become a separate ProductMeasurement.",
  "verification_caveats": "Algorithm-driven optimization campaigns may include sequential or iterative experiments, possibly in flow, where time ordering and multiple responses per run matter. Multi-objective outcomes (yield, purity, impurities) have to be mapped onto ORD measurement types."
}