{
  "abstract": "BN-based materials in modern electronics, quantum photonics, and energy devices. Despite their technological importance, the atomistic mechanisms underlying its phase evolution remain poorly understood. Here, we use large-scale molecular dynamics simulations with a MACE-based machine-learning interatomic potential to investigate phase-transformation pathways in crystalline BN. Trained on high-fidelity density functional theory (DFT) data and validated against ab initio molecular dynamics simulations, the potential enables simulations of phase evolution across extended length and time scales. We identify a layer-by-layer transformation mechanism that initiates at interfaces, where lattice mismatch and local structural disorder promote reconstructive transformations. We further show that transformation propensity depends non-monotonically on interfacial energy and is enhanced by compressive stresses generated during thermal treatment. These results provide atomistic insight into BN phase transformations under diverse thermodynamic conditions, while the associated MACE training workflow offers a generalizable framework for studying phase-transformation mechanisms in other complex materials.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry",
    "University of Texas at Austin",
    "Laboratory for Energy Applications for the Future (LEAF",
    "Lawrence Livermore National Laboratory",
    "Materials Science Division",
    "Oden Institute for Computational Engineering & Sciences"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Jaeyoung Cho"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Nikhil Rampal",
      "orcid": "0000-0003-2143-5874"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Stephen E. Weitzner"
    },
    {
      "affs": [
        0,
        1,
        5
      ],
      "name": "Graeme Henkelman"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Liwen F. Wan",
      "orcid": "0000-0002-5391-0804"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f3t4d1t0gakcs.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008201/v1",
  "fulltext_url": null,
  "id": "f3t4d1t0gakcs",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008201/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|Boron and Carbon Nanomaterials Research"
    ]
  ],
  "openalex": "W7205007680",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/f3t4d1t0gakcs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008201",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008201/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Boron and Carbon Nanomaterials Research",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Unveiling Phase Transformation Mechanism in BN from Large-Scale Machine Learning Molecular Dynamics",
  "title_text": "Unveiling Phase Transformation Mechanism in BN from Large-Scale Machine Learning Molecular Dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/f3t4d1t0gakcs",
  "venue": "ChemRxiv",
  "versions": []
}