{
  "abstract": "Deterministic synthesis of borophene remains challenging because many polymorphs compete during nucleation and growth. Here we combine a reactive machine-learned interatomic potential with grand-canonical Monte Carlo simulations and data-driven structural classification to track borophene formation from early nuclei to extended layers on Ag(111) and Ag(100). We build temperature-pressure substrate growth maps and resolve how vacancy motifs, phase intermixing and seed structure govern polymorph selection. The simulations reproduce key experimental trends, including the prevalence of $β_{12}$/$χ_3$ phases and their temperature-dependent competition, while revealing kinetic pathways that connect metastable nuclei to long-range order. We identify conditions that suppress competing motifs and promote targeted phases, providing actionable synthesis windows. These results establish a predictive framework for directing borophene growth and, more broadly, for controlling polymorphism in low-dimensional materials by coupling atomistic simulation with machine-learning-enabled phase recognition.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.03886",
  "author_total": 4,
  "authors": [
    {
      "name": "Colin Bousige"
    },
    {
      "name": "Jean Furstoss"
    },
    {
      "name": "Julien Lam"
    },
    {
      "name": "Pierre Mignon"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/36bkn8ddndpxd.bib",
  "cited_by": 2,
  "doi": "10.48550/arxiv.2606.03886",
  "fulltext_url": null,
  "id": "36bkn8ddndpxd",
  "issns": [],
  "landing_url": "https://hal.science/hal-05842241v1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.chim"
    ],
    [
      "hal",
      "1.chim.chem"
    ],
    [
      "openalex",
      "2505|Boron and Carbon Nanomaterials Research"
    ]
  ],
  "openalex": "W7163346469",
  "pdf_url": "https://hal.science/hal-05842241/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/36bkn8ddndpxd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "hal-05842241",
      "source": "hal",
      "url": "https://hal.science/hal-05842241v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Boron and Carbon Nanomaterials Research",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Deciphering borophene growth pathways with data-driven simulations",
  "title_text": "Deciphering borophene growth pathways with data-driven simulations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/36bkn8ddndpxd",
  "venue": "HAL",
  "versions": []
}