{
  "abstract": "Auger-electron spectroscopy has long accompanied x-ray photoelectron spectroscopy as a second modality to resolve chemical states with overlapping core-electron binding energies. However, analyzing the Auger spectrum is challenged by its complexity and the computational expense of its simulation. Here we demonstrate that the physical connection, and thus inter-task relationship, between the generation of a core-hole and its corresponding Auger-Meitner decay enables inductive knowledge transfer through the training of a multi-task graph neural network to predict both observables from a common graph embedding. Both task losses are combined with learned weights via the uncertainty weighting procedure. Overall, the single-task and multi-task models predict calculated and experimental Auger lineshapes with good accuracy in most cases. The performance between the two task regimes is similar, with the single-task models generally having better predictions of the finer peak structures in the spectrum. The present results demonstrate that multi-task training is a promising avenue for future developments of universal x-ray spectroscopy models with learned representations that map the molecular structure to a multitude of techniques.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.16339",
  "author_total": 3,
  "authors": [
    {
      "name": "Adam E. A. Fouda"
    },
    {
      "name": "Patrick Phillips"
    },
    {
      "name": "Phay J. Ho"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/97vn3b6gxymej.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.16339",
  "fulltext_url": null,
  "id": "97vn3b6gxymej",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.16339",
  "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",
      "physics.chem-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.16339",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/97vn3b6gxymej.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:41Z",
      "native_id": "2609.16339",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.16339"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Multi-Task Graph Neural Network Predictions of Auger-Electron and X-ray Photoelectron Spectroscopy",
  "title_text": "Multi-Task Graph Neural Network Predictions of Auger-Electron and X-ray Photoelectron Spectroscopy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:41Z",
  "url": "https://research.draconitic.com/paper/97vn3b6gxymej",
  "venue": "arXiv",
  "versions": []
}