{
  "abstract": "Complex and high entropy alloys are attracting much attention currently thanks to their mechanical and corrosion resistance properties in harsh environments, in particular needed for carbon-free energy applications. However, their elaboration in bulk and in thin film form in a trial-and-error approach is impractical due to their complexity and the cocktail effect. The recent development of artificial intelligence brings a new possibility for their elaboration and adjustment of their properties. Firstly, we present an overview of Materials and data science research. Then we describe how DIADEM - French initiative for Materials and Data science convergence - tackles the development of innovative coatings for carbon-free energy applications (nuclear, high temperature electrolysis, ...) thanks to the development of a nationwide network of synthesis and characterization platforms - the DIADEM discovery hub. We describe in particular DIADEM-2D, an AI-driven Hybrid HiPIMS/Pulsed-DC PVD process using 4 cathodes in confocal combinatorial configuration. We present the high entropy alloy determination using data from the literature for corrosion resistance in molten salt media and nuclear accidental conditions. An element-independent model gathering deposition parameters and coating properties has been implemented allowing the design of protective coatings with a particular composition. We demonstrate the feasibility of this process and its accuracy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Vacuum 255 (2027) 115852",
  "arxiv": "2608.01903",
  "author_total": 7,
  "authors": [
    {
      "name": "Paul Foulquier"
    },
    {
      "name": "Ryma Haddad"
    },
    {
      "name": "Ali Mahmoud"
    },
    {
      "name": "Eric Monsifrot"
    },
    {
      "name": "Fanny Balbaud-Celerier"
    },
    {
      "name": "Jean-Philippe Poli"
    },
    {
      "name": "Frederic Schuster"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4fb041sfwcp35.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.01903",
  "fulltext_url": null,
  "id": "4fb041sfwcp35",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.01903",
  "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"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2608.01903",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4fb041sfwcp35.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:15Z",
      "native_id": "2608.01903",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.01903"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Machine Learning Assisted Design of Complex and High Entropy Alloys by Hybrid HiPIMS/Pulsed-DC PVD Process for Low Carbon Energy Applications in Extreme Environments",
  "title_text": "Machine Learning Assisted Design of Complex and High Entropy Alloys by Hybrid HiPIMS/Pulsed-DC PVD Process for Low Carbon Energy Applications in Extreme Environments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:15Z",
  "url": "https://research.draconitic.com/paper/4fb041sfwcp35",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.vacuum.2026.115852",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}