{
  "abstract": "Closed-loop automated experimentation platforms are accelerating materials characterization and discovery, but their efficiency hinges on a question that automation alone does not answer: given a fixed evaluation budget, which experiment configurations should be run next? Batch active learning addresses this by selecting a group of candidates at each iteration, yet the design choices that govern its efficiency, how many candidates to acquire per cycle, and how to balance exploitation against exploration remain poorly understood. Here, we present a systematic in-silico benchmark of batch active learning across optimization landscapes, prediction models, and dimensionalities, introducing composite metrics that track optimization progress and surrogate accuracy as co-equal outcomes. We find that an exploitative-dominant strategy with a small fixed exploratory minority matches or exceeds purely exploitative configurations while outperforming purely exploratory ones across landscapes, models, and dimensionalities, and that batchand cycle-efficient regimes are distinct and constraint-dependent. We further validate these findings on an automated coacervate condensate optimization platform, confirming their transfer to a real, noisy materials system.",
  "abstract_shown": true,
  "affiliations": [
    "Inst. Complex Molecular Systems, Dept. Biomedical Engineering, Eindhoven University of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Andrea Gardin"
    },
    {
      "affs": [
        0
      ],
      "name": "Willem van den Hout"
    },
    {
      "affs": [
        0
      ],
      "name": "Yannick H. A. Leurs"
    },
    {
      "affs": [
        0
      ],
      "name": "Luc Brunsveld"
    },
    {
      "affs": [
        0
      ],
      "name": "JanC. M. van Hest"
    },
    {
      "affs": [
        0
      ],
      "name": "Francesca Grisoni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ax3x1jsfvwcmw.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008883/v1",
  "fulltext_url": null,
  "id": "ax3x1jsfvwcmw",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008883/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": "W7213309330",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ax3x1jsfvwcmw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008883",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008883/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "A Systematic Approach to Batch Active Learning for Material Optimization",
  "title_text": "A Systematic Approach to Batch Active Learning for Material Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/ax3x1jsfvwcmw",
  "venue": "ChemRxiv",
  "versions": []
}