{
  "abstract": "Electrosynthesis has surfaced as a key approach in modern synthesis, enabling more sustainable chemical transformations with reduced chemical waste. The accurate prediction of electrosynthesis performance is crucial for accelerating the discovery and design of reactions. Despite indisputable advances, the descriptor-based predictions fall short in representing the full reaction regime due to the high‑dimensional parameter space of electrosynthesis. Cyclic voltammograms, which are typically employed for mechanistic analysis, inherently contain rich information about electrochemical processes. Herein, we applied computer vision techniques to collect a cyclic voltammogram image database and trained a neural network directly on these image data. The resulting models achieved reliable performance in predicting electrosynthesis outcomes, demonstrating the potential of image-based deep learning for electrochemical reaction evaluation and data-driven electrosynthesis studies.",
  "abstract_shown": true,
  "affiliations": [
    "Georg‑August‑Universität Göttingen, Tammannstraße 2, Göttingen, Germany, 37077",
    "State Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization",
    "Anhui Agricultural University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xinran Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Sven Erik Peters"
    },
    {
      "affs": [
        0
      ],
      "name": "Lutz Ackermann"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/epyj9djjrbynk.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009343/v1",
  "fulltext_url": null,
  "id": "epyj9djjrbynk",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009343/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7214129201",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009343/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/epyj9djjrbynk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009343",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009343/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Computer Vision for Predicting Electrochemical Reaction Performance from Electroanalytical Data",
  "title_text": "Computer Vision for Predicting Electrochemical Reaction Performance from Electroanalytical Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/epyj9djjrbynk",
  "venue": "ChemRxiv",
  "versions": []
}