{
  "abstract": "Atomristors, non-volatile resistive switching devices based on two-dimensional (2D) monolayers, are promising building blocks for energy-efficient memory and neuromorphic computing. However, their design remains restricted to a few materials such as MoS2 and h-BN, limiting functional diversity and design flexibility. Here, a high-throughput computational framework combining density functional theory, machine-learning molecular dynamics, and quantum transport simulations screens about 2,900 exfoliable monolayers for vacancy-mediated resistive switching, identifying 17 thermally stable candidates in two mechanistically distinct classes. In Class 1 monolayers, such as GaS, Au adsorption at the native vacancy introduces conducting states, switching the insulating monolayer from a high- to a low-resistance state (HRS-to-LRS). Class 2 monolayers, comprising ionically bonded metal oxyhalides and nitrohalides such as BiOCl, exhibit previously unreported inverted switching. Vacancy-released electrons delocalize and push the Fermi level into the conduction band, placing the device natively in the LRS; Au adsorption re-localizes these carriers and returns the Fermi level to the gap, driving LRS-to-HRS switching. Quantum transport simulations confirm both mechanisms, while migration-barrier calculations identify the electrode-2D separation as a key parameter governing Au migration and the resistance window. These findings expand the atomristor landscape and establish complementary switching as a design paradigm for multifunctional memory and neuromorphic hardware.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.03578",
  "author_total": 11,
  "authors": [
    {
      "name": "Sanchali Mitra"
    },
    {
      "name": "Arnab Kabiraj"
    },
    {
      "name": "Benjamin W. J. Chen"
    },
    {
      "name": "Han Zhang"
    },
    {
      "name": "Haiyu Meng"
    },
    {
      "name": "Shi-Jun Liang"
    },
    {
      "name": "C. S. Lau"
    },
    {
      "name": "Lei Shen"
    },
    {
      "name": "Lain-Jong Li"
    },
    {
      "name": "Kah-Wee Ang"
    },
    {
      "name": "Yee Sin Ang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fkf0het593v8w.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.03578",
  "fulltext_url": null,
  "id": "fkf0het593v8w",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.03578",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2208|Advanced Memory and Neural Computing"
    ]
  ],
  "openalex": "W7208740459",
  "pdf_url": "https://arxiv.org/pdf/2609.03578",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fkf0het593v8w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:31Z",
      "native_id": "2609.03578",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.03578"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "High-Throughput Computational Discovery of Inverted Resistive Switching in Two-Dimensional Materials",
  "title_text": "High-Throughput Computational Discovery of Inverted Resistive Switching in Two-Dimensional Materials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:31Z",
  "url": "https://research.draconitic.com/paper/fkf0het593v8w",
  "venue": "arXiv",
  "versions": []
}