{
  "abstract": "The efficiency of Bayesian optimization (BO) of atomic configurations depends strongly on how configurations are encoded. We introduce the Weisfeiler-Lehman (WL) subtree kernel, which views configurations as element-labeled graphs and measures their similarity by how many local structural patterns they share, into Bayesian-optimization-based configuration search. Because this kernel is reproduced as the plain inner product of explicit features (L$^2$-normalized histograms of local topological patterns), introducing it reduces to introducing the corresponding features: the encoding enters existing BO frameworks as an ordinary descriptor. In a benchmark ground-state configuration search of cubic BC$_2$N evaluated with a universal machine-learning interatomic potential, the WL encoding reached the ground state almost immediately after a shared random initialization of 100 samples in every one of five independent rounds (108$\\pm$5 evaluations on average), whereas the one-hot baseline required 280$\\pm$122 evaluations; the WL-driven sampler first exhausted the degenerate ground-state group and then discovered the metastable degenerate groups from the bottom up, in order of increasing energy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.00953",
  "author_total": 4,
  "authors": [
    {
      "name": "Akira Kusaba"
    },
    {
      "name": "Tatoshi Yonemori"
    },
    {
      "name": "Tetsuji Kuboyama"
    },
    {
      "name": "Yoshihiro Kangawa"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e22tgd3m7zh1z.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.00953",
  "fulltext_url": null,
  "id": "e22tgd3m7zh1z",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.00953",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7206190758",
  "pdf_url": "https://arxiv.org/pdf/2609.00953",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e22tgd3m7zh1z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2609.00953",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.00953"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Weisfeiler-Lehman subtree encoding for Bayesian optimization of atomic configurations",
  "title_text": "Weisfeiler-Lehman subtree encoding for Bayesian optimization of atomic configurations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/e22tgd3m7zh1z",
  "venue": "arXiv",
  "versions": []
}