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Peer-reviewedCC BY-NCRSC Advances

A physics-driven machine learning framework for predicting XRD patterns, magnetization, and magnetocaloric response in rare-earth-doped double perovskite oxides

  • Sana Meftah
  • Emna Ammar Elhadjamor
  • Souhir Bouzidi
  • Sonia Soltani
  • Amel Haouas
  • Jean Juraszek
  • Jemai Dhahri
Open at sourceDOICite

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.

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Cite

BibTeXRIS
BibTeX
@article{meftah2026physicsdriven,
  title = {{A physics-driven machine learning framework for predicting XRD patterns, magnetization, and magnetocaloric response in rare-earth-doped double perovskite oxides}},
  author = {Sana Meftah and Emna Ammar Elhadjamor and Souhir Bouzidi and Sonia Soltani and Amel Haouas and Jean Juraszek and Jemai Dhahri},
  journal = {RSC Advances},
  year = {2026},
  month = sep,
  doi = {10.1039/d6ra04290b},
  url = {https://hal.science/hal-05745679v1},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - A physics-driven machine learning framework for predicting XRD patterns, magnetization, and magnetocaloric response in rare-earth-doped double perovskite oxides
AU  - Sana Meftah
AU  - Emna Ammar Elhadjamor
AU  - Souhir Bouzidi
AU  - Sonia Soltani
AU  - Amel Haouas
AU  - Jean Juraszek
AU  - Jemai Dhahri
PY  - 2026
DA  - 2026/09/10
JO  - RSC Advances
DO  - 10.1039/d6ra04290b
UR  - https://hal.science/hal-05745679v1
AB  - > 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.
SN  - 2046-2069
ER  -