{
  "abstract": "Scientific priors speed up Bayesian optimisation in materials discovery, but a prior that steers a small budget toward the materials it favours may miss those it disfavours. We measured this in a benchmark with a registered analysis plan: 7,020 searches (3,000 of them over 3,550 metallic multielement Materials Project structures), for the shear modulus and the specific Young’s modulus, with a historical prior and a composition-only elemental-modulus heuristic, each mixed into expected improvement search with a fixed weight β. Coverage was the recall of the half of the top-5% materials that the prior itself scores lowest. Coverage loss at moderate weight was detected in 4 of 6 prior–objective settings. The elemental heuristic reduced the restricted mean number of acquisitions to a top-1% shear-modulus material by 13.9 at β = 0.25 with no detected loss of coverage, but its shear-modulus form, applied to the specific modulus, lost 44.0% or more of the baseline coverage at every nonzero weight. A weight both beneficial and safe under the registered criterion existed in 3 of 6 settings (2 under a post hoc joint error control), never above β = 0.25, and depended on the safety margin and on curation. At β = 0.5, in 5 of 6 settings the weighted search found more top-5% materials in total but, by its estimates, fewer of those its prior disfavours (a loss detected in 3): the prior redirected the search rather than reducing its yield. Faster discovery alone therefore does not show that a search keeps what its prior disfavours.",
  "abstract_shown": true,
  "affiliations": [
    "Independent researcher"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Muhammad Asad",
      "orcid": "0009-0005-7632-2641"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8qhjnsgbphxp6.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15011035/v1",
  "fulltext_url": null,
  "id": "8qhjnsgbphxp6",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011035/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": "W7223701114",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011035/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/8qhjnsgbphxp6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15011035",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011035/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "The efficiency–coverage trade-off of scientific priors in Bayesian search: a registered benchmark on metallic multi-element elasticity data",
  "title_text": "The efficiency–coverage trade-off of scientific priors in Bayesian search: a registered benchmark on metallic multi-element elasticity data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/8qhjnsgbphxp6",
  "venue": "ChemRxiv",
  "versions": []
}