{
  "abstract": "Diamond is an important host material for functional defects such as solid-state spin qubits. During the defect creation by irradiation and subsequent thermal annealing, carbon-related defects, including vacancies and interstitials, are generated and can migrate to form new defect complexes. To investigate this kinetically driven process and reveal the formation mechanisms of the di-interstitials and di-vacancies in the neutral charge state, we fine-tuned a universal machine learning interatomic potential using a small dataset generated from density functional theory calculations. We obtained a barrier of 1.84 eV for mono-interstitial migration and found that two initially separated mono-interstitials only need to overcome a lower barrier to form di-interstitial configurations. Beyond the training data, the MLIP also identified the formation pathway for an additional di-interstitial configuration and a stepwise divacancy dissociation mechanism. These results reveal migration-assisted defect complex formation mechanisms and demonstrate how targeted MLIP training can identify reaction pathways.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.26301",
  "author_total": 4,
  "authors": [
    {
      "name": "Grace Huang"
    },
    {
      "name": "Christopher Fivecoat"
    },
    {
      "name": "Xun Xu"
    },
    {
      "name": "Mengen Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9d4b7s30vjvfm.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.26301",
  "fulltext_url": null,
  "id": "9d4b7s30vjvfm",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.26301",
  "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|Diamond and Carbon-based Materials Research"
    ]
  ],
  "openalex": "W7223067982",
  "pdf_url": "https://arxiv.org/pdf/2610.26301",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9d4b7s30vjvfm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:52Z",
      "native_id": "2610.26301",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.26301"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Machine Learning-Assisted Discovery of Carbon-Related Defect Formation Mechanisms in Diamond",
  "title_text": "Machine Learning-Assisted Discovery of Carbon-Related Defect Formation Mechanisms in Diamond",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:41Z",
  "url": "https://research.draconitic.com/paper/9d4b7s30vjvfm",
  "venue": "arXiv",
  "versions": []
}