{
  "abstract": "Self-driving labs typically optimize experiments within a predefined design space. Yet high-throughput campaigns can fail for reasons outside this design space, in-cluding reagent state, stock solution quality, and handling history. Here, we present a case study of a five-plate high-throughput gold nanorod synthesis campaign in which a multimodal large language model (LLM) helped diagnose such cryptic failure modes. We used low-cost camera images for rapid assessment of seed and synthesis state, with UV–vis spectroscopy when the images were insufficient for failure diagnosis. The campaign included an abnormal seed batch, a visually plausible synthesis with hidden spectroscopic defects, and a plate-wide failed synthesis in which most wells showed weak spectra with a strong rise toward the near-infrared. In response, the LLM intervened by questioning the seed preparation step and reagent handling instead of optimizing the screened concentrations of silver nitrate and ascorbic acid. To quantify recovery, we defined a UV–vis spectral roughness metric based on local spectral interpolation across the synthesis grid. The final synthesis had the lowest roughness, Rspec = 0.636 compared with ∼ 0.772 for earlier successful runs, and the highest median longitudinal-window absorbance. Our results show the potential of multimodal LLMs upstream of autonomous optimization in self-driving labs to identify when an experiment has failed for reasons outside the nominal design space and when additional characterization is needed before optimization should continue.",
  "abstract_shown": true,
  "affiliations": [
    "Chemical and Biomolecular Engineering",
    "Institute of Technology",
    "Electrical and Computer Engineering"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Riya Patel"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Gabriela Briceno"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Amirali Aghazadeh",
      "orcid": "0000-0003-0223-0873"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Vida Jamali",
      "orcid": "0000-0002-0022-8449"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cm0xmaxp761g8.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009205/v1",
  "fulltext_url": null,
  "id": "cm0xmaxp761g8",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009205/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7213979959",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009205/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/cm0xmaxp761g8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009205",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009205/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Multimodal LLM-Assisted Diagnosis of Cryptic Failure Modes in High-Throughput Experimentation: A Case Study in Gold Nanorod Synthesis",
  "title_text": "Multimodal LLM-Assisted Diagnosis of Cryptic Failure Modes in High-Throughput Experimentation: A Case Study in Gold Nanorod Synthesis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/cm0xmaxp761g8",
  "venue": "ChemRxiv",
  "versions": []
}