{
  "abstract": "> 0.99 across the full range of temperatures and applied magnetic fields. These results highlight a property-dependent complementarity between ensemble and neural network approaches, providing critical guidance for optimal algorithm selection in complex oxide systems. This work establishes a physics-guided data-driven computational framework for the LCENMO compound, paving the way toward the investigation of magnetocaloric properties in this material.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Sana Meftah"
    },
    {
      "name": "Emna Ammar Elhadjamor"
    },
    {
      "name": "Souhir Bouzidi"
    },
    {
      "name": "Sonia Soltani"
    },
    {
      "name": "Amel Haouas"
    },
    {
      "name": "Jean Juraszek"
    },
    {
      "name": "Jemai Dhahri"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4hvp9z7d1jpkq.bib",
  "cited_by": 0,
  "doi": "10.1039/d6ra04290b",
  "fulltext_url": null,
  "id": "4hvp9z7d1jpkq",
  "issns": [
    "2046-2069"
  ],
  "landing_url": "https://hal.science/hal-05745679v1",
  "language": "en",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.chim"
    ],
    [
      "hal",
      "0.phys"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7204089880",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": "Royal Society of Chemistry",
  "ris": "https://research.draconitic.com/paper/4hvp9z7d1jpkq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "hal-05745679",
      "source": "hal",
      "url": "https://hal.science/hal-05745679v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "A physics-driven machine learning framework for predicting XRD patterns, magnetization, and magnetocaloric response in rare-earth-doped double perovskite oxides",
  "title_text": "A physics-driven machine learning framework for predicting XRD patterns, magnetization, and magnetocaloric response in rare-earth-doped double perovskite oxides",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:33Z",
  "url": "https://research.draconitic.com/paper/4hvp9z7d1jpkq",
  "venue": "RSC Advances",
  "versions": []
}