Modeling magnetic refrigeration capacity of doped EuTiO3 magnetocaloric compounds using swarm based intelligent computational method

Abstract

This work models the magnetic refrigeration capacity (MRC) of doped europium titanate (EuTiO3) at different applied magnetic field using hybrid particle swarm optimization based support vector regression (SW-SVR) algorithm with ionic radii dopant descriptors. The developed model with Gaussian function (SW-SVR-Gas) shows better performance as compared with polynomial based model (SW-SVR-Pol) with improvement of 35.76 %, 0.13 % and 45.76 % using mean absolute error (MAE), correlation coefficient (CC) and root mean square error (RMSE) performance yardsticks, respectively. The developed SW-SVR-Gas model investigates the behavior of Eu1-xBaxTiO3 and EuTi1-xNixO3 doped europium titanate at different dopants concentration as well as applied field. Novelties of the developed model include circumvention of experimental difficulties associated with magnetic refrigeration capacity determination, implementation of simple molecular descriptors with high precision and accuracy. The demonstrated uniqueness of the developed model would strengthen exploration of refrigeration capacity of europium titanate based compounds for addressing global refrigeration crisis.