{
  "abstract": "Autonomous experimentation offers a time-efficient route towards accelerated catalyst discovery, but current practices often rely on black-box models that are difficult to interpret. Here we demonstrate the paradigm of interpretable autonomous exploration of a 12-dimensional space in the electrocatalytic hydrogen evolution reaction by integrating an interpretable decision-making artificial intelligence with a fully automated electrochemical platform. Guided by a human’s chemistry-informed hypothesis, a chemistry-infused bandit is established to reflect the locally quadratic approximation between synthetic conditions and electrocatalytic activities near the explored optimum region. Not only does such a decision-making bandit lead to the discovery of previously unreported, earth-abundant electrocatalysts at least comparable to Pt-based ones, the bandit also reveals model-derived hypothesis for how synthetic conditions affect the electrocatalysts’ performance near the identified local optima as autonomous inquiry proceeds, which is otherwise untenable from black-box ones. Incorporating chemistry-informed hypothesis into the decision-making bandit is generally applicable to any chemistry problem and offers a framework for autonomous investigation, advancing hypothesis generation for catalyst’s design principles beyond simple optimization.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "name": "Yi-An Lai",
      "orcid": "0009-0001-5604-0816"
    },
    {
      "name": "Muxin Xiong"
    },
    {
      "name": "Jingwen Sun",
      "orcid": "0000-0003-3657-7672"
    },
    {
      "name": "Aijian Huang",
      "orcid": "0000-0002-3483-3233"
    },
    {
      "name": "Max Van Fleet"
    },
    {
      "name": "Hongyuan Sheng",
      "orcid": "0000-0002-0494-4418"
    },
    {
      "name": "Jennifer Kim",
      "orcid": "0000-0003-0040-0285"
    },
    {
      "name": "Felisha Kuo"
    },
    {
      "name": "Tai Ying Lai",
      "orcid": "0000-0002-2766-9199"
    },
    {
      "name": "Xuian-Rou Lin"
    },
    {
      "name": "Yu-An Chen"
    },
    {
      "name": "Weitong Zhang",
      "orcid": "0000-0003-4731-9986"
    },
    {
      "name": "Hao Ming Chen"
    },
    {
      "name": "Chong Liu",
      "orcid": "0000-0001-5546-3852"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6tr1qr3dwph7f.bib",
  "cited_by": 0,
  "doi": "10.1038/s41467-026-78335-w",
  "fulltext_url": null,
  "id": "6tr1qr3dwph7f",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://www.nature.com/articles/s41467-026-78335-w",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7166667221",
  "pdf_url": "https://www.nature.com/articles/s41467-026-78335-w_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/6tr1qr3dwph7f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:39Z",
      "native_id": "10.1038/s41467-026-78335-w",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41467-026-78335-w"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Chemistry-infused bandit framework for interpretable autonomous campaign of electrocatalysis",
  "title_text": "Chemistry-infused bandit framework for interpretable autonomous campaign of electrocatalysis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:39Z",
  "url": "https://research.draconitic.com/paper/6tr1qr3dwph7f",
  "venue": "Nature Communications",
  "versions": [
    {
      "doi": "10.26434/chemrxiv.15005418/v1",
      "pid": null,
      "relation": "preprint",
      "title": null,
      "venue": null
    }
  ]
}