{
  "abstract": "Autonomous laboratories for two-dimensional semiconductors pair a recipe planner with instruments that return in situ spectra every few seconds. Some decisions are too fast for the planner: stop a failing selenization anneal, flag a flake that is not the logged material, or end an ion irradiation at a set vacancy density, from a few examples, on a small computer at the instrument. The fruit fly takes decisions of this form with two structures whose wiring is measured at synapse level: the mushroom body, a one-shot memory for odours, and the ventral nerve cord, which turns a few descending commands into local reflexes. We asked which instrument jobs models wired from these connectomes do better than engineered tools. A mushroom-body model of 685 projection neurons and 5,177 Kenyon cells read one Raman band per glomerulus; over 20 paired seeds, on simulated selenization Raman streams and on Ar + irradiation anchored in published in situ spectra, it lost the fewest flakes when one failed run taught a stop rule (3.3 of 30, against 3.9 for a nearest neighbour), read vacancy density from its sparse code with less error than from the raw spectrum (5.5 % against 6.4 %), and ran in 0.30 ms on a Raspberry Pi 5 within 152 kB. A stop decided at the furnace, as the nerve cord decides reflexes, prevented the losses of a planner that needed 60 s per turn (0.15 against 10.7 flakes of 30). It did not choose recipes. In three animals and after training, measured wiring matched a degree-preserving shuffle, and the nerve-cord wiring matched a ridge regression: the connectome contributes an architecture and wiring statistics, not a partner map. As hardware, the fixed wiring can be binary and each plastic synapse needs about 64 levels. The connectome supplies a low-cost reflex layer under the planner, not a replacement.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Materials Science and Engineering",
    "University of Utah"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Kinston Ackölf",
      "orcid": "0000-0002-5579-4878"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Taylor D. Sparks"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ev5gk4ec5qrh1.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010667/v1",
  "fulltext_url": null,
  "id": "ev5gk4ec5qrh1",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010667/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": "W7222733105",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010667/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ev5gk4ec5qrh1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010667",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010667/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "How the fruit fly connectome can help autonomous materials discovery",
  "title_text": "How the fruit fly connectome can help autonomous materials discovery",
  "type": "preprint",
  "updated_at": "2026-11-04T00:20:58Z",
  "url": "https://research.draconitic.com/paper/ev5gk4ec5qrh1",
  "venue": "ChemRxiv",
  "versions": []
}