{
  "abstract": "Extracting spectral properties such as energy position, broadening, and spectral weight from dielectric spectra is critical for interpreting collective electronic excitations in many-body physics. Conventional approaches typically rely on nonlinear fitting procedures that can be computationally demanding and highly sensitive to initialization and fitting choices. In this work, we develop a convolutional neural-network framework for the direct inversion of dielectric spectra into their underlying plasmonic pole structures using a multipole-Padé representation. Rather than training on a constrained database of spectra associated with a specific set of materials, the network is trained entirely on synthetic spectra generated from the analytical multipole-Padé expression with randomized parameters. This enables the network to learn the general mapping between the spectra and their features in an unbiased way, without requiring large, material-specific datasets derived from real materials. We demonstrate that the synthetically trained network generalizes to complex first-principles and experimental dielectric spectra, extracting the underlying pole parameters with high accuracy in a single forward pass. This approach provides an efficient and robust alternative to conventional nonlinear fitting, enabling high-throughput, automated analysis of dielectric spectra across diverse materials and applications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.11698",
  "author_total": 1,
  "authors": [
    {
      "name": "Dario A. Leon"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cmnw2srxftpzj.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.11698",
  "fulltext_url": null,
  "id": "cmnw2srxftpzj",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.11698",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "physics.optics"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7221174300",
  "pdf_url": "https://arxiv.org/pdf/2610.11698",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cmnw2srxftpzj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:54Z",
      "native_id": "2610.11698",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.11698"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Predicting plasmonic pole representations of complex dielectric spectra using convolutional neural networks",
  "title_text": "Predicting plasmonic pole representations of complex dielectric spectra using convolutional neural networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:54Z",
  "url": "https://research.draconitic.com/paper/cmnw2srxftpzj",
  "venue": "arXiv",
  "versions": []
}