{
  "abstract": "The martensitic transformations in stoichiometric Ni$_2$MnGa and related Heusler alloys are governed by a delicate interplay between electronic structure, magnetism, and lattice dynamics. While density functional theory (DFT) has successfully mapped the energy landscape of modulated martensites, it remains computationally prohibitive for capturing finite-temperature dynamics across relevant length and time scales. Here, we bridge this gap by constructing DFT-trained machine-learning force-fields (ML-FF) interpolated from ab initio molecular dynamics. We demonstrate that ML-FF trained on DFT data can reproduce key features of Ni$_2$MnGa, including the soft TA$_2$ phonon and a preference for nanotwinned martensites. Using classical molecular dynamics simulations, we explore the impact of adaptive nanotwinning on the austenite-martensite transition, which facilitates the dynamics of both, forward and backward transformation. We demonstrate, that the negative twin boundary energy along the entire transformations path predicted by DFT for $T=0$, supports the formation of ordered modulated martensites at finite temperatures. Our results pave the way for large-scale simulations of magnetic shape memory alloys with near-DFT accuracy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16467",
  "author_total": 5,
  "authors": [
    {
      "name": "Markus E. Gruner"
    },
    {
      "name": "Yulia Klunnikova"
    },
    {
      "name": "Mike J. Bruckhoff"
    },
    {
      "name": "Konstatin P. Skokov"
    },
    {
      "name": "Olga N. Miroshkina"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dm1cvgtjk0s1d.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16467",
  "fulltext_url": null,
  "id": "dm1cvgtjk0s1d",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16467",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2505|Shape Memory Alloy Transformations"
    ]
  ],
  "openalex": "W7221769648",
  "pdf_url": "https://arxiv.org/pdf/2610.16467",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dm1cvgtjk0s1d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2610.16467",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16467"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Modelling the dynamics of the martensitic transformation in Ni$_2$MnGa with machine-learning force-fields",
  "title_text": "Modelling the dynamics of the martensitic transformation in Ni_2MnGa with machine-learning force-fields",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/dm1cvgtjk0s1d",
  "venue": "arXiv",
  "versions": []
}