Work

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

Year: 2026

Type: article

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.

Source: RSC Advances

DOI: 10.1039/d6ra04290b

Authors: Sana Meftah, Emna Ammar Elhadjamor, Souhir Bouzidi, Sonia Soltani, Amel Haouas, Jean Juraszek, Jemai Dhahri

Institutions: University of Monastir, Manouba University, Qassim University, Northern Border University, Centre National de la Recherche Scientifique, Groupe de Physique des Matériaux, Université de Rouen Normandie, Institut National des Sciences Appliquées Rouen Normandie

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,042

OpenAlex ID: W7204089880

FWCI:

Cites: 45

Cited by:

Related to: 10