{
  "abstract": "New catalysts are often discovered by trial and error, a search in which most candidates fail. Machine learning now shortens that search by predicting how untested formulations perform from composition alone. Measuring the physicochemical properties that govern performance should shorten it further, yet this expectation remains untested in a discovery campaign. We tested it on Pt-based propane dehydrogenation catalysts, evaluating discovery speed, prediction accuracy, and physical insight on the same materials. Here we show that measured properties neither accelerate the search nor improve predictions beyond composition alone. However, they resolve deactivation into separable contributions from exposed platinum and surface acidity. These results explain why brute-force screening dominates industry and define, in catalysis and beyond, when to characterize a material rather than test the next one.",
  "abstract_shown": true,
  "affiliations": [
    "Ralph O’Connor Sustainable Energy Institute",
    "Johns Hopkins University",
    "Department of Chemical and Biomolecular Engineering",
    "Department of Chemistry"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Ricardo Mathison",
      "orcid": "0000-0001-7093-5811"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Harsh R. Darji",
      "orcid": "0000-0001-6713-0174"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Jacob Huppin",
      "orcid": "0009-0001-4569-673X"
    },
    {
      "affs": [
        0,
        1,
        3
      ],
      "name": "Fillipp Edvard L. Salvador",
      "orcid": "0009-0005-7484-3339"
    },
    {
      "affs": [
        0,
        1,
        3
      ],
      "name": "Faysal Ibrahim",
      "orcid": "0000-0003-3354-8104"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Matias Alvear",
      "orcid": "0009-0009-9379-9555"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Ive Hermans",
      "orcid": "0000-0001-6228-9928"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2hgh4jm9vd9bg.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15011162/v1",
  "fulltext_url": null,
  "id": "2hgh4jm9vd9bg",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011162/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7224119724",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/2hgh4jm9vd9bg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15011162",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011162/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Is characterization worth the cost in data-driven catalyst discovery?",
  "title_text": "Is characterization worth the cost in data-driven catalyst discovery?",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/2hgh4jm9vd9bg",
  "venue": "ChemRxiv",
  "versions": []
}