{
  "abstract": "The selectivity of electrocatalytic nitrite reduction to hydroxylamine is governed by multiple coupled steps within the multi-electron nitrogen–oxygen reduction network, which cannot be represented simultaneously by a volcano relation based on a single adsorption descriptor. To address this limitation, a catalyst discovery workflow coupling machine-learned interatomic potentials (MLIPs) with multi-objective Bayesian optimization (MOBO) is developed. A small set of physical descriptors maps discrete oxide–dopant candidates onto a low-dimensional continuous representation, while four thermodynamic objectives describe the competing steps in NH2OH formation. The MLIP provides unified, low-cost structural and energy evaluation for oxide candidates. A Gaussian process surrogate model together with expected hypervolume improvement sequentially selects candidates from the discrete pool and updates the Pareto front within a fixed evaluation budget. The workflow integrates chemical objectives, energy evaluation, and sequential decision-making for catalyst screening in oxide systems.",
  "abstract_shown": true,
  "affiliations": [
    "Ministry of Education; School of Chemical and Material Engineering",
    "Jiangnan University",
    "School of Computer Science and Technology",
    "Xidian University",
    "School of Geography and Environmental Management",
    "University of Waterloo"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhiyi Yu",
      "orcid": "0009-0008-6545-3066"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Huaiwen Zhang"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Weiming Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bvdntfzbncf33.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009355/v1",
  "fulltext_url": null,
  "id": "bvdntfzbncf33",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009355/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": "W7214092726",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009355/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/bvdntfzbncf33.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009355",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009355/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "A Catalyst Discovery Workflow Coupling MLIP with MOBO for Nitrite-to-Hydroxylamine Electroreduction",
  "title_text": "A Catalyst Discovery Workflow Coupling MLIP with MOBO for Nitrite-to-Hydroxylamine Electroreduction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/bvdntfzbncf33",
  "venue": "ChemRxiv",
  "versions": []
}