{
  "abstract": "This study provides a data-driven analysis of a novel dataset of single-cell Anion Exchange Membrane water electrolyzers (AEMWE), operated under constant current load across multiple heterogeneous experimental campaigns. We train and evaluate a range of machine learning models with different complexity, including linear baselines, LSTMs and CNNs, to perform medium-term forecasting of the cell voltage degradation curve. The models are assessed within a rigorous training and evaluation framework specifically designed for heterogeneous industrial data.",
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  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.37941",
  "author_total": 6,
  "authors": [
    {
      "name": "Marco Veneriano"
    },
    {
      "name": "Ani Gjergji"
    },
    {
      "name": "Sebastiano Bellani"
    },
    {
      "name": "Andrea Riva"
    },
    {
      "name": "Vito Paolo Pastore"
    },
    {
      "name": "Matteo Santacesaria"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/08srcqhw9b323.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.37941",
  "fulltext_url": null,
  "id": "08srcqhw9b323",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.37941",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "physics.app-ph"
    ],
    [
      "openalex",
      "2208|Fuel Cells and Related Materials"
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  ],
  "openalex": "W7214955507",
  "pdf_url": "https://arxiv.org/pdf/2609.37941",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/08srcqhw9b323.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.37941",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.37941"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.app-ph",
      "subfield": "applied-instrumentation"
    }
  ],
  "title": "An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis",
  "title_text": "An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/08srcqhw9b323",
  "venue": "arXiv",
  "versions": []
}