{
  "abstract": "Recent years have seen a strong push towards data-driven discovery of materials in many areas, from structural alloys to energy devices to quantum materials. However, many applications involve prolonged exposure to aqueous and/or acidic electrochemical conditions that can promote material degradation. Despite the critical role of stability in determining material performance, there is a lack of models that are capable of predicting a material’s corrosion-passivation behavior without relying on empirical parameters or fitting. In this work, we address this gap by developing an ab initio kinetic model (AKM), which integrates an existing empirical passivation growth theory, the Point Defect Model (PDM), into a non-empirical first principles-based framework. We redefine the five primary inputs of the PDM, previously ad hoc or experimentally-fitted, into ab initio -calculable parameters, entirely removing their empirical dependence. We then apply AKM to predict the passivation growth of six common metal-film systems. In comparison to experiments reported in the literature, we obtain a strong qualitative trend and reproduce the correct orders of magnitude, without any a priori experimental inputs. We further investigate passive-film formation in the Cu-CuO x system through a detailed computational case study, establishing guidelines for selecting appropriate surface terminations, oxygen vacancy configurations, and point defect concentrations. Sensitivity analyses further reveal that, within the PDM framework, the electrical driving force has a greater influence on passive-film growth than the chemical driving force. This work represents a vital step towards the development of high-throughput predictive modeling of materials’ electrochemical stability in applications where materials are exposed to aqueous media.",
  "abstract_shown": true,
  "affiliations": [
    "Materials Science and Engineering",
    "Fulton Schools of Engineering, Arizona State University",
    "Department of Physics",
    "Arizona State University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Rachel Gorelik"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Arunima K. Singh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6c82157y3hr0a.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009521/v1",
  "fulltext_url": null,
  "id": "6c82157y3hr0a",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009521/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": "W7214617757",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/6c82157y3hr0a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009521",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009521/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "An Ab Initio-Kinetic Framework for Predicting Electrochemical Passivation of Materials",
  "title_text": "An Ab Initio-Kinetic Framework for Predicting Electrochemical Passivation of Materials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/6c82157y3hr0a",
  "venue": "ChemRxiv",
  "versions": []
}