{
  "abstract": "Defect engineering is a crucial tool for tuning and tailoring the properties of two-dimensional (2D) materials. A key challenge is identifying which defects are kinetically stable at room temperature and sufficiently promising to warrant further investigation. However, identifying stable defects with suitable properties using first-principles calculations, such as density functional theory (DFT) is computationally demanding, effectively limiting the scope of high-throughput studies. In this work, we investigate whether the machine learning interatomic potential MACE can reproduce sorption trends (adsorption vs. absorption) previously obtained from high-throughput DFT calculations and extend the analysis to elevated temperatures. A total of 17,624 defect structures from the ADAQ database are simulated using MACE-accelerated molecular dynamics (MD) at both 0 K and 300 K. The results show that MACE successfully reproduces the overall sorption trends observed in DFT calculations at 0 K at a fraction of the computational cost. Simulations performed at 300 K further indicate that the qualitative sorption trends remain largely the same when including finite-temperature effects. These findings demonstrate that MACE-driven MD can be used as an effective high-throughput prescreening tool for defect-related studies in 2D materials, specially related to sorption. While conventional first-principles methods, such as DFT, remain necessary for accurate and quantitative validation, the use of MLIPs enables MD simulations of finite-temperature defect behavior at a fraction of the computational cost of DFT-based MD.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20063",
  "author_total": 9,
  "authors": [
    {
      "name": "Nils-Ruben Skogemyr"
    },
    {
      "name": "Albin Sjöstedt"
    },
    {
      "name": "Isak Wernroth"
    },
    {
      "name": "Isak Häggqvist"
    },
    {
      "name": "Isac Ljungwald"
    },
    {
      "name": "Hans-Ove Åkesson"
    },
    {
      "name": "Henricus R. A. ten Eikelder"
    },
    {
      "name": "Rickard Armiento"
    },
    {
      "name": "Joel Davidsson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4x4k75nrqg5t2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20063",
  "fulltext_url": null,
  "id": "4x4k75nrqg5t2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20063",
  "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": "W7221881943",
  "pdf_url": "https://arxiv.org/pdf/2610.20063",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4x4k75nrqg5t2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "2610.20063",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20063"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Sorption Molecular Dynamics of Point Defects in 2D Materials with a Machine Learning Potential",
  "title_text": "Sorption Molecular Dynamics of Point Defects in 2D Materials with a Machine Learning Potential",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:31Z",
  "url": "https://research.draconitic.com/paper/4x4k75nrqg5t2",
  "venue": "arXiv",
  "versions": []
}