Machine learning assisted electrocaloric and pyroelectric performance of Ba0.85Ca0.15Ti0.9Zr0.1O3 ceramic for solid state refrigeration

2026-01-23

Manoj Nayak, Barun Haldar, Hillol Joardar, Tanmaya Badapanda, Subhashree Sahoo, Nashmi H. Alrasheedi, Ahmed Abdala,
Machine learning assisted electrocaloric and pyroelectric performance of Ba0.85Ca0.15Ti0.9Zr0.1O3 ceramic for solid state refrigeration,
Ceramics International,
Volume 51, Issue 27, Part A,
2025,
Pages 51803-51817,
ISSN 0272-8842,
https://doi.org/10.1016/j.ceramint.2025.08.392.
(https://www.sciencedirect.com/science/article/pii/S0272884225041999)
Abstract: In the present study, a thorough investigation was carried out to assess the electrocaloric performance of Ba0.85Ca0.15Zr0.10Ti0.9O3 (BCZT) ceramics synthesized via the mixed oxide route, incorporating machine learning tools for enhanced analysis. The Rietveld refinement of the ceramics' X-ray diffraction (XRD) patterns verifies the simultaneous existence of two phases (orthogonal and tetragonal symmetries). The micrograph obtained from the scanning electron microscope indicates a well-defined and dense grain. The temperature variant dielectric exhibits two phase transformations associated with orthorhombic symmetry to tetragonal symmetry, and tetragonal symmetry to cubic symmetry. The electric field variant ferroelectric hysteresis was measured at various temperatures, and the remnant polarization and coercive field decreased with Temperature. The temperature variant polarization values at different electric fields were modelled using various machine learning approaches. With minimal experimental input, the expected outcomes enable efficient and reliable prediction of electrocaloric behaviour. Thermodynamic Maxwell relations were employed to indirectly evaluate variations in isothermal entropy, adiabatic Temperature, and electrocaloric strength. Different figures of merit, like relative refrigerant capacity, cooling power, and entropy change aggregated over Temperature, are analyzed across varying electric fields, highlighting the material's promise for environmentally friendly cooling technologies.
Keywords: Ferroelectric; Machine learning; Electrocaloric effect; Pyroelectric coefficient