{
  "abstract": "In this work, we combine first-principles calculations, ab initio molecular dynamics (AIMD), and machine-learning-interatomic-potential molecular dynamics (MLIP-MD) to investigate lithium storage and transport in two-dimensional Cu$_8$B$_{14}$, including an experimentally identified line-defect configuration. Pristine Cu$_8$B$_{14}$ remains metallic upon lithiation and exhibits favourable Li adsorption, a single-surface theoretical capacity of 427~mAh~g$^{-1}$, and an average open-circuit voltage of approximately 0.53~V. The lowest Li migration barrier on the pristine surface is 0.32~eV. The line-defected structure retains a capacity of approximately 400~mAh~g$^{-1}$ while reducing the local migration barrier to 0.21~eV. To access Li dynamics beyond the picosecond time scale of AIMD, a pretrained MACE-MP-0 model was fine-tuned against system-specific DFT energies and forces and employed in nanosecond-scale simulations from 400 to 600~K. Both pristine and line-defected lithiated monolayers remain structurally stable over this temperature range. At 400~K, the line-defected system exhibits a higher in-plane Li tracer diffusivity ($3.97\\times10^{-5}$~cm$^{2}$~s$^{-1}$) than the pristine monolayer ($2.50\\times10^{-5}$~cm$^{2}$~s$^{-1}$), whereas the pristine system becomes more diffusive at 500 and 600~K. Arrhenius analysis yields effective activation energies of 0.147 and 0.225~eV for the line-defected and pristine systems, respectively. These results show that the line defect modifies the Li-transport landscape and its temperature dependence rather than uniformly enhancing long-range diffusion, while preserving competitive Li-storage capacity and structural stability.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.12824",
  "author_total": 4,
  "authors": [
    {
      "name": "Subhasis Sarkar"
    },
    {
      "name": "Rajnendra Singh"
    },
    {
      "name": "Brahmananda Chakraborty"
    },
    {
      "name": "Sridhar Sahu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/71jbsqcf6khab.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.12824",
  "fulltext_url": null,
  "id": "71jbsqcf6khab",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.12824",
  "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",
      "2208|Advancements in Battery Materials"
    ]
  ],
  "openalex": "W7203455417",
  "pdf_url": "https://arxiv.org/pdf/2608.12824",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/71jbsqcf6khab.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:52Z",
      "native_id": "2608.12824",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.12824"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Unraveling Lithium Storage and Defect-Modulated Transport in 2D Copper Boride: From First Principles to Machine-Learned Molecular Dynamics",
  "title_text": "Unraveling Lithium Storage and Defect-Modulated Transport in 2D Copper Boride: From First Principles to Machine-Learned Molecular Dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:52Z",
  "url": "https://research.draconitic.com/paper/71jbsqcf6khab",
  "venue": "arXiv",
  "versions": []
}