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Phase diagrams construction using mean-field renormalization and neural network fitting

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  • Rodríguez, Juan Esteban Bedoya
  • Diaz, Leon Escobar
  • Hernandez, Sebastian Trujillo

Abstract

Using the mean-field renormalization group (MFRG) method, we study the magnetic phase behavior of the disordered alloys FepMn0.6−pAl0.4 and FepAl1−p. Chemical disorder and competing magnetic interactions are modeled within a diluted Ising framework, and for the FepAl1−p system second-neighbor interactions are incorporated for the first time within the MFRG approach. To connect the theoretical state equations with experimental data, we employ a neural-network-based fitting strategy in which the exchange interaction functions are represented as flexible parametric forms constrained by the MFRG equations, in a manner conceptually related to physics-informed neural networks. The resulting phase diagrams are in good agreement with experimental observations, reproducing critical temperatures and phase boundaries, while the inferred exchange energies exhibit physically consistent trends. These results demonstrate that the combined MFRG–neural network approach provides a promising framework for modeling disordered magnetic alloys.

Suggested Citation

  • Rodríguez, Juan Esteban Bedoya & Diaz, Leon Escobar & Hernandez, Sebastian Trujillo, 2026. "Phase diagrams construction using mean-field renormalization and neural network fitting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 692(C).
    • Handle: RePEc:eee:phsmap:v:692:y:2026:i:c:s0378437126002554
    DOI: 10.1016/j.physa.2026.131519
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